Upload app.py

app.py

@@ -187,8 +187,8 @@ y2_explainer_rf = shap.TreeExplainer(y2_model_rf)
 
 from catboost import CatBoostClassifier
 cb = CatBoostClassifier(**y3_params)
-y3_model_xgb = cb.fit(x3, y3)
-y3_explainer_xgb = shap.TreeExplainer(y3_model_xgb)
+y3_model_cb = cb.fit(x3, y3)
+y3_explainer_cb = shap.TreeExplainer(y3_model_xgb)
 
 from lightgbm import LGBMClassifier
 lgb = LGBMClassifier(**y4_params)
@@ -255,7 +255,7 @@ def y2_predict_cb(*args):
     return {"Non-home Discharge": float(pos_pred[0][1]), "Home Discharge": float(pos_pred[0][0])}
 
 def y2_predict_rf(*args):
-    df2 = pd.DataFrame([args], columns=x2_rf.columns)
+    df2 = pd.DataFrame([args], columns=x2.columns)
     df2 = df2.astype({col: "category" for col in categorical_columns2})
     d2 = dict.fromkeys(df2.select_dtypes(np.int64).columns, np.int32)
     df2 = df2.astype(d2)
@@ -286,7 +286,7 @@ def y3_predict_cb(*args):
     return {"Prolonged LOS": float(pos_pred[0][1]), "No Prolonged LOS": float(pos_pred[0][0])}
 
 def y3_predict_rf(*args):
-    df3 = pd.DataFrame([args], columns=x3_rf.columns)
+    df3 = pd.DataFrame([args], columns=x3.columns)
     df3 = df3.astype({col: "category" for col in categorical_columns3})
     d3 = dict.fromkeys(df3.select_dtypes(np.int64).columns, np.int32)
     df3 = df3.astype(d3)
@@ -317,7 +317,7 @@ def y4_predict_cb(*args):
     return {"Prolonged ICU-LOS": float(pos_pred[0][1]), "No Prolonged ICU-LOS": float(pos_pred[0][0])}
 
 def y4_predict_rf(*args):
-    df4 = pd.DataFrame([args], columns=x4_rf.columns)
+    df4 = pd.DataFrame([args], columns=x4.columns)
     df4 = df4.astype({col: "category" for col in categorical_columns4})
     d4 = dict.fromkeys(df4.select_dtypes(np.int64).columns, np.int32)
     df4 = df4.astype(d4)
@@ -384,7 +384,7 @@ def y1_interpret_xgb(*args):
     return fig
 
 def y1_interpret_lgb(*args):
-    df1 = pd.DataFrame([args], columns=x1_lgb.columns)
+    df1 = pd.DataFrame([args], columns=x1.columns)
     df1 = df1.astype({col: "category" for col in categorical_columns1})
     shap_values1 = y1_explainer_lgb.shap_values(df1)
     shap_values1 = np.abs(shap_values1)