clean up comments

Author: isabelizimm

vetiver/handlers/sklearn.py

@@ -33,14 +33,10 @@ def handler_predict(self, input_data, check_prototype):
         prediction
             Prediction from model
         """
-        # if check_prototype:
-        #     input_data = api_data_to_frame(input_data)
 
         if not check_prototype or isinstance(input_data, pd.DataFrame):
             prediction = self.model.predict(input_data)
         else:
             prediction = self.model.predict([input_data])
 
-        # some sort of post-prediction/pre send back to user hook
-
         return prediction.tolist()

vetiver/handlers/statsmodels.py

@@ -42,9 +42,6 @@ def handler_predict(self, input_data, check_prototype):
         if not sm_exists:
             raise ImportError("Cannot import `statsmodels`")
 
-        # if check_prototype:
-        #     input_data = api_data_to_frame(input_data)
-
         if isinstance(input_data, (list, pd.DataFrame)):
             prediction = self.model.predict(input_data)
         else:

vetiver/handlers/torch.py

@@ -43,11 +43,8 @@ def handler_predict(self, input_data, check_prototype):
             raise ImportError("Cannot import `torch`.")
 
         if check_prototype:
-            # input_data = api_data_to_frame(input_data)
             input_data = np.array(input_data, dtype=np.array(self.prototype_data).dtype)
             prediction = self.model(torch.from_numpy(input_data))
-
-        # do not check ptype
         else:
             input_data = torch.tensor(input_data)
             prediction = self.model(input_data)

vetiver/handlers/xgboost.py

@@ -43,14 +43,12 @@ def handler_predict(self, input_data, check_prototype):
         if not xgb_exists:
             raise ImportError("Cannot import `xgboost`")
 
-        # if check_prototype:
-        #     input_data = api_data_to_frame(input_data)
-
         if not isinstance(input_data, pd.DataFrame):
             try:
                 input_data = pd.DataFrame(input_data)
             except ValueError:
                 raise (f"Expected a dict or DataFrame, got {type(input_data)}")
+
         input_data = xgboost.DMatrix(input_data)
 
         prediction = self.model.predict(input_data)