Summary interaction plots gen for OVER

explicability/shap_plots.py

@@ -3,6 +3,7 @@
 import pandas as pd
 import numpy as np
 import shap
+import matplotlib.pyplot as plt
 # --------------------------------------------------------------------------------------------------------
 
 # Reading test data
@@ -37,18 +38,25 @@ if __name__ == "__main__":
         2: "OVER",
         3: "UNDER"
     }
+    model_choices = {
+        "ORIG": "XGB",
+        "ORIG_CW": "RF",
+        "OVER": "XGB",
+        "UNDER": "XGB"
+    }
     # --------------------------------------------------------------------------------------------------------
 
     # Plot 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_']):
+    for j, method in enumerate(['', '', 'over_', 'under_']):
+        if method != 'over_':
+            continue
+        for i, group in enumerate(['pre', 'post']):
+            X_test = data_dic['X_test_' + group]
+            y_test = data_dic['y_test_' + group]
             print(f"{group}-{method_names[j]}")
             method_name = method_names[j]
-            shap_vals = np.load(f'./output/shap_values/{group}_{method_name}.npy')
-            # print(f'Loaded SHAP values. Shape: {shap_vals.shape}')
+            model_name = model_choices[method_name]
             shap_inter_vals = np.load(f'./output/shap_inter_values/{group}_{method_name}.npy')
-            # print(f'Loaded SHAP INTER values. Shape: {shap_inter_vals.shape}')
+            shap.summary_plot(shap_inter_vals, X_test, max_display=15)
+            plt.savefig(f'./output/plots/inters/{group}_{method_name}_{model_name}.svg', format='svg', dpi=1250)
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