Upload app.py

app.py

@@ -175,10 +175,10 @@ y4_params =  {'objective': 'binary', 'booster': 'gbtree', 'lambda': 9.0811397283
 y5_params =  {'objective': 'binary', 'boosting_type': 'gbdt', 'lambda_l1': 0.0016190622681086678, 'lambda_l2': 0.00041749233000407354, 'num_leaves': 2, 'feature_fraction': 0.5730231365909909, 'bagging_fraction': 0.6964002116636187, 'bagging_freq': 6, 'min_child_samples': 44, 'metric': 'binary_logloss', 'verbosity': -1, 'random_state': 31}
 
 #Training models.
-from xgboost import XGBClassifier
-y1_xgb = XGBClassifier(**y1_params)
-y1_model_xgb = y1_xgb.fit(x1, y1)
-y1_explainer_xgb = shap.TreeExplainer(y1_model_xgb)
+from lightgbm import LGBMClassifier
+lgb = LGBMClassifier(**y4_params)
+y4_model_lgb = lgb.fit(x4, y4)
+y4_explainer_lgb = shap.TreeExplainer(y4_model_lgb)
 
 from sklearn.ensemble import RandomForestClassifier as rf
 y2_rf = rf(**y2_params)
@@ -325,34 +325,34 @@ def y4_predict_rf(*args):
     return {"Mortality": float(pos_pred[0][1]), "No Mortality": float(pos_pred[0][0])}
 
 #Define predict for y5 (complications).
-def y4_predict_xgb(*args):
-    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.int34)
-    df4 = df4.astype(d4)
-    pos_pred = y4_model_xgb.predict_proba(df4)
+def y5_predict_xgb(*args):
+    df5 = pd.DataFrame([args], columns=x5.columns)
+    df5 = df5.astype({col: "category" for col in categorical_columns5})
+    d5 = dict.fromkeys(df5.select_dtypes(np.int64).columns, np.int35)
+    df5 = df5.astype(d5)
+    pos_pred = y5_model_xgb.predict_proba(df5)
     return {"Mortality": float(pos_pred[0][1]), "No Mortality": float(pos_pred[0][0])}
 
-def y4_predict_lgb(*args):
-    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.int34)
-    df4 = df4.astype(d4)
-    pos_pred = y4_model_lgb.predict_proba(df4)
+def y5_predict_lgb(*args):
+    df5 = pd.DataFrame([args], columns=x5.columns)
+    df5 = df5.astype({col: "category" for col in categorical_columns5})
+    d5 = dict.fromkeys(df5.select_dtypes(np.int64).columns, np.int35)
+    df5 = df5.astype(d5)
+    pos_pred = y5_model_lgb.predict_proba(df5)
     return {"Mortality": float(pos_pred[0][1]), "No Mortality": float(pos_pred[0][0])}
 
-def y4_predict_cb(*args):
-    df4 = pd.DataFrame([args], columns=x4.columns)
-    df4 = df4.astype({col: "category" for col in categorical_columns4})
-    pos_pred = y4_model_cb.predict(Pool(df4, cat_features = categorical_columns4), prediction_type='Probability')
+def y5_predict_cb(*args):
+    df5 = pd.DataFrame([args], columns=x5.columns)
+    df5 = df5.astype({col: "category" for col in categorical_columns5})
+    pos_pred = y5_model_cb.predict(Pool(df5, cat_features = categorical_columns5), prediction_type='Probability')
     return {"Mortality": float(pos_pred[0][1]), "No Mortality": float(pos_pred[0][0])}
 
-def y4_predict_rf(*args):
-    df4 = pd.DataFrame([args], columns=x4_rf.columns)
-    df4 = df4.astype({col: "category" for col in categorical_columns4})
-    d4 = dict.fromkeys(df4.select_dtypes(np.int64).columns, np.int34)
-    df4 = df4.astype(d4)
-    pos_pred = y4_model_rf.predict_proba(df4)
+def y5_predict_rf(*args):
+    df5 = pd.DataFrame([args], columns=x5_rf.columns)
+    df5 = df5.astype({col: "category" for col in categorical_columns5})
+    d5 = dict.fromkeys(df5.select_dtypes(np.int64).columns, np.int35)
+    df5 = df5.astype(d5)
+    pos_pred = y5_model_rf.predict_proba(df5)
     return {"Mortality": float(pos_pred[0][1]), "No Mortality": float(pos_pred[0][0])}
 
 #Define function for wrapping feature labels.