Working and ready to generate shap and shap interaction values

explicability/shap_vals_testing.py

@@ -117,6 +117,41 @@ def get_chosen_model(group_str, method_str, model_name):
     return chosen_model, is_tree
 # --------------------------------------------------------------------------------------------------------
 
+# Get balanced subset of n elements from original datasets
+# --------------------------------------------------------------------------------------------------------
+
+def get_sample(X_train, y_train, X_test, y_test, n):
+    # Convert numpy arrays to pandas series for easier handling if necessary
+    y_train = pd.Series(y_train)
+    y_test = pd.Series(y_test)
+    
+    # Concatenate X and y for train and test to make it easier to work with
+    train = pd.concat([X_train, y_train.rename('target')], axis=1)
+    test = pd.concat([X_test, y_test.rename('target')], axis=1)
+    
+    # Get n/2 samples from each class for the training set
+    train_0 = train[train['target'] == 0].sample(n//2)
+    train_1 = train[train['target'] == 1].sample(n//2)
+    
+    # Concatenate the samples to form the balanced training set
+    balanced_train = pd.concat([train_0, train_1])
+    
+    # Get n/2 samples from each class for the testing set
+    test_0 = test[test['target'] == 0].sample(n//2)
+    test_1 = test[test['target'] == 1].sample(n//2)
+    
+    # Concatenate the samples to form the balanced testing set
+    balanced_test = pd.concat([test_0, test_1])
+    
+    # Separate the features and the target variable for both sets
+    X_train_balanced = balanced_train.drop('target', axis=1)
+    y_train_balanced = balanced_train['target']
+    X_test_balanced = balanced_test.drop('target', axis=1)
+    y_test_balanced = balanced_test['target']
+    
+    return X_train_balanced, y_train_balanced, X_test_balanced, y_test_balanced
+# --------------------------------------------------------------------------------------------------------
+
 if __name__ == "__main__":
 
     # Setup
@@ -147,12 +182,15 @@ if __name__ == "__main__":
     y_test = data_dic['y_test_' + group]
     X_train = data_dic['X_train_' + method + group]
     y_train = data_dic['y_train_' + method + group]
+
+    X_train, y_train, X_test, y_test = get_sample(X_train, y_train, X_test, y_test, 500)
+
     method_name = 'UNDER'
     # Get chosen tuned model for this group and method context
     model, is_tree = get_chosen_model(group_str=group, method_str=method_name, model_name=model_choices[method_name])
     fit_start_t = time.time()
     # Fit model with training data
-    fitted_model = model.fit(X_train[:500], y_train[:500])
+    fitted_model = model.fit(X_train, y_train)
     fit_end_t = time.time()
     print(f'Fitted OK. Took {fit_end_t-fit_start_t} seconds.')
     # Check if we are dealing with a tree vs nn model
@@ -164,18 +202,18 @@ if __name__ == "__main__":
     shap_start_t = time.time()
     # Compute shap values
     shap_val_start_t = time.time()
-    shap_vals = explainer.shap_values(X_test[:500], check_additivity=False) # Change to true for final results
+    shap_vals = explainer.shap_values(X_test, check_additivity=False) # Change to true for final results
     shap_val_end_t = time.time()
     print(f'Shap values computed. Took {shap_val_end_t-shap_val_start_t} seconds.')
     # Compute shap interaction values
-    shap_interaction_values = explainer.shap_interaction_values(X_test[:500])
-    print(f'Shape: {shap_interaction_values.shape}')
+    shap_interaction_values = explainer.shap_interaction_values(X_test)
+    print(f'Shape Interaction Values: {shap_interaction_values.shape}')
     shap_end_t = time.time()
     print(f'Interaction values computed. Took {shap_end_t - shap_start_t} seconds.')
     # Plot interaction values accross variables
     plot_start_t = time.time()
-    shap.summary_plot(shap_interaction_values, X_test[:500], max_display=5)
+    shap.summary_plot(shap_interaction_values, X_test, max_display=39)
     plot_end_t = time.time()
     print(f'Plot done. Took {plot_end_t - plot_start_t} seconds.')
-    plt.savefig('shap_summary_plot.svg', dpi=1000)
+    plt.savefig('shap_summary_plot.svg', dpi=2000)
     plt.close()
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