Upload app.py

app.py

@@ -148,32 +148,24 @@ y2 = x2.pop('OUTCOME')
 categorical_columns2 = list(x2.select_dtypes('object').columns)
 le = sklearn.preprocessing.LabelEncoder()
 x2[categorical_columns2] = x2[categorical_columns2].apply(le.fit_transform)
-d2 = dict.fromkeys(x2.select_dtypes(np.int64).columns, str)
-x2 = x2.astype(d2)
 
 #Prepare data for the outcome 3 (LOS).
 y3 = x3.pop('OUTCOME')
 categorical_columns3 = list(x3.select_dtypes('object').columns)
 le = sklearn.preprocessing.LabelEncoder()
 x3[categorical_columns3] = x3[categorical_columns3].apply(le.fit_transform)
-d3 = dict.fromkeys(x3.select_dtypes(np.int64).columns, str)
-x3 = x3.astype(d3)
 
 #Prepare data for the outcome 4 (ICU LOS).
 y4 = x4.pop('OUTCOME')
 categorical_columns4 = list(x4.select_dtypes('object').columns)
 le = sklearn.preprocessing.LabelEncoder()
 x4[categorical_columns4] = x4[categorical_columns4].apply(le.fit_transform)
-d4 = dict.fromkeys(x4.select_dtypes(np.int64).columns, str)
-x4 = x4.astype(d4)
 
 #Prepare data for the outcome 5 (complications).
 y5 = x5.pop('OUTCOME')
 categorical_columns5 = list(x5.select_dtypes('object').columns)
 le = sklearn.preprocessing.LabelEncoder()
 x5[categorical_columns5] = x5[categorical_columns5].apply(le.fit_transform)
-d5 = dict.fromkeys(x5.select_dtypes(np.int64).columns, str)
-x5 = x5.astype(d5)
 
 #Assign hyperparameters.
 y1_params = {'objective': 'binary:logistic', 'booster': 'gbtree', 'lambda': 0.5059844209148782, 'alpha': 0.0030156848979492556, 'max_depth': 2, 'eta': 4.546875002603483e-07, 'gamma': 1.1982641538268563e-08, 'grow_policy': 'lossguide', 'eval_metric': 'auc', 'verbosity': 0, 'seed': 31}