lint

Author: isabelizimm

.pre-commit-config.yaml

@@ -10,7 +10,7 @@ repos:
         types:
           - python
         args:
-          - "--max-line-length=90"
+          - "--max-line-length=100"
       - id: trailing-whitespace
       - id: end-of-file-fixer
       - id: check-yaml

vetiver/handlers/sklearn.py

@@ -30,8 +30,8 @@ def handler_predict(self, input_data, check_prototype: bool, **kw):
             Test data
         check_prototype: bool
         prediction_type: str
-            Type of prediction to make. One of "predict", "predict_proba", or "predict_log_proba".
-            Default is "predict".
+            Type of prediction to make. One of "predict", "predict_proba",
+            or "predict_log_proba". Default is "predict".
 
         Returns
         -------
@@ -40,7 +40,14 @@ def handler_predict(self, input_data, check_prototype: bool, **kw):
         """
         prediction_type = kw.get("prediction_type", "predict")
         if prediction_type not in ["predict", "predict_proba", "predict_log_proba"]:
-            raise ValueError('prediction_type must be "predict", "predict_proba", or "predict_log_proba"')
-
-        input_data = [input_data] if check_prototype and not isinstance(input_data, pd.DataFrame) else input_data
+            raise ValueError(
+                'prediction_type must be "predict", "predict_proba", \
+                    or "predict_log_proba"'
+            )
+
+        input_data = (
+            [input_data]
+            if check_prototype and not isinstance(input_data, pd.DataFrame)
+            else input_data
+        )
         return getattr(self.model, prediction_type)(input_data).tolist()

vetiver/handlers/statsmodels.py

@@ -42,6 +42,8 @@ def handler_predict(self, input_data, check_prototype, **kw):
         """
         if not sm_exists:
             raise ImportError("Cannot import `statsmodels`")
-        
-        input_data = input_data if isinstance(input_data, (list, pd.DataFrame)) else [input_data]
+
+        input_data = (
+            input_data if isinstance(input_data, (list, pd.DataFrame)) else [input_data]
+        )
         return self.model.predict(input_data).tolist()