fix bug on shap values for numerical values

autoML.py

@@ -92,9 +92,10 @@ def autoML(csv, task, budget, label, metric_to_minimize_class, metric_to_minimiz
 
     tab1, tab2, tab3, tab4 = st.tabs(["AutoML", "Best Model", "Partial Dependence", "Shap Values"])
 
+
     with tab1:
 
-        time_history, best_valid_loss_history, valid_loss_history, config_history, metric_history = get_output_from_log(filename=log, time_budget=120)
+        time_history, best_valid_loss_history, _, config_history, _ = get_output_from_log(filename=log, time_budget=120)
     
         def model(s):
             mod = s.get('Current Learner')
@@ -132,6 +133,7 @@ def autoML(csv, task, budget, label, metric_to_minimize_class, metric_to_minimiz
         st.write('Estimator tested')
         st.table(automl.estimator_list)
 
+
     with tab2:
         st.header('Best Model')
 
@@ -171,6 +173,7 @@ def autoML(csv, task, budget, label, metric_to_minimize_class, metric_to_minimiz
 
         download_model(automl)
     
+
     with tab3:
         with st.container():
             st.subheader('1D Partial Dependance for the three most important features')
@@ -242,13 +245,15 @@ def autoML(csv, task, budget, label, metric_to_minimize_class, metric_to_minimiz
 
         df_features_test = df_test[df_test.columns.difference([label])]
 
-        with st.spinner(f'Compute Shap Values'):
-            explainer = shap.Explainer(pipeline.predict, df_features_test)
-            shap_values = explainer(df_features_test)
-
-        st.set_option('deprecation.showPyplotGlobalUse', False)
-        st.pyplot(shap.plots.beeswarm(shap_values))
-        st.pyplot(shap.summary_plot(shap_values, plot_type='violin'))
+        with st.container():
+            with st.spinner(f'Compute Shap Values...'):
+                explainer = shap.Explainer(pipeline.predict, df_features_test)
+                shap_values = explainer(df_features_test)
+            st.subheader('Beeswarm Plot')
+            plt.figure()
+            st.pyplot(shap.plots.beeswarm(shap_values, show=False).figure)
+            #st.divider()
+            #st.pyplot(shap.plots.violin(shap_values, show=False).figure)