Fix repr problem for PredictionIntervalOutliersTransform

tinkoff-ai · Feb 22, 2022 · f54d6bd · f54d6bd
1 parent ee64934
commit f54d6bd
Show file tree

Hide file tree

Showing 2 changed files with 5 additions and 5 deletions.
diff --git a/etna/core/mixins.py b/etna/core/mixins.py
@@ -16,14 +16,14 @@ def __repr__(self):
                 continue
             elif param.kind == param.VAR_KEYWORD:
                 for arg_, value in self.__dict__[arg].items():
-                    args_str_representation += f"{arg_} = {value.__repr__()}, "
+                    args_str_representation += f"{arg_} = {repr(value)}, "
             else:
                 try:
                     value = self.__dict__[arg]
                 except KeyError as e:
                     value = None
                     warnings.warn(f"You haven't set all parameters inside class __init__ method: {e}")
-                args_str_representation += f"{arg} = {value.__repr__()}, "
+                args_str_representation += f"{arg} = {repr(value)}, "
         return f"{self.__class__.__name__}({args_str_representation})"
 
 

diff --git a/etna/datasets/tsdataset.py b/etna/datasets/tsdataset.py
@@ -134,7 +134,7 @@ def transform(self, transforms: Sequence["Transform"]):
         self._check_endings(warning=True)
         self.transforms = transforms
         for transform in self.transforms:
-            tslogger.log(f"Transform {transform.__repr__()} is applied to dataset")
+            tslogger.log(f"Transform {repr(transform)} is applied to dataset")
             columns_before = set(self.columns.get_level_values("feature"))
             self.df = transform.transform(self.df)
             columns_after = set(self.columns.get_level_values("feature"))
@@ -145,7 +145,7 @@ def fit_transform(self, transforms: Sequence["Transform"]):
         self._check_endings(warning=True)
         self.transforms = transforms
         for transform in self.transforms:
-            tslogger.log(f"Transform {transform.__repr__()} is applied to dataset")
+            tslogger.log(f"Transform {repr(transform)} is applied to dataset")
             columns_before = set(self.columns.get_level_values("feature"))
             self.df = transform.fit_transform(self.df)
             columns_after = set(self.columns.get_level_values("feature"))
@@ -288,7 +288,7 @@ def make_future(self, future_steps: int) -> "TSDataset":
 
         if self.transforms is not None:
             for transform in self.transforms:
-                tslogger.log(f"Transform {transform.__repr__()} is applied to dataset")
+                tslogger.log(f"Transform {repr(transform)} is applied to dataset")
                 df = transform.transform(df)
 
         future_dataset = df.tail(future_steps).copy(deep=True)