Update predictor.py

predictor.py

@@ -134,8 +134,8 @@ def make_a_prediction(X):
 predictions = make_a_prediction(X_test)
 print(predictions)
 
-#to be called when needed
-def create_shap_models(data):
+#to be called when needed for optimized results
+def create_opt_shap_models(data):
     explainer = shap.TreeExplainer(model)
     shap_values = explainer.shap_values(data)
     shap.initjs()
@@ -152,6 +152,25 @@ def create_shap_models(data):
     interaction_values = explainer.shap_interaction_values(data)
     interaction_values[0].round(2)
     st.write(pd.DataFrame(interaction_values[0].round(2)).head(60))
-
     
-    return obj1
+    return obj1
+    #to be called when needed for optimized results
+def create_unopt_shap_models(data):
+    explainer = shap.TreeExplainer(lgbm)
+    shap_values = explainer.shap_values(data)
+    shap.initjs()
+    obj2 = shap.force_plot(explainer.expected_value, shap_values=shap_values, feature_names=data.columns)
+    
+    shap.initjs()
+    shap.decision_plot(explainer.expected_value, shap_values, feature_names=np.array(data.columns))
+    st.pyplot(bbox_inches='tight')
+    
+    shap.initjs()
+    shap.summary_plot(shap_values=shap_values, feature_names=data.columns)
+    st.pyplot(bbox_inches='tight')
+    
+    interaction_values = explainer.shap_interaction_values(data)
+    interaction_values[0].round(2)
+    st.write(pd.DataFrame(interaction_values[0].round(2)).head(60))
+    
+    return obj2