Replace warning with error

etna/transforms/encoders/categorical.py

@@ -1,4 +1,3 @@
-import warnings
 from enum import Enum
 from typing import List
 from typing import Optional
@@ -78,7 +77,7 @@ def __init__(self, in_column: str, out_column: Optional[str] = None, strategy: s
     def get_regressors_info(self) -> List[str]:
         """Return the list with regressors created by the transform."""
         if self.in_column_regressor is None:
-            warnings.warn("Regressors info might be incorrect. Fit the transform to get the correct regressors info.")
+            raise ValueError("Fit the transform to get the correct regressors info!")
         return [self._get_column_name()] if self.in_column_regressor else []
 
     def _fit(self, df: pd.DataFrame) -> "LabelEncoderTransform":
@@ -159,7 +158,7 @@ def __init__(self, in_column: str, out_column: Optional[str] = None):
     def get_regressors_info(self) -> List[str]:
         """Return the list with regressors created by the transform."""
         if self.in_column_regressor is None:
-            warnings.warn("Regressors info might be incorrect. Fit the transform to get the correct regressors info.")
+            raise ValueError("Fit the transform to get the correct regressors info!")
         return self._get_out_column_names() if self.in_column_regressor else []
 
     def _fit(self, df: pd.DataFrame) -> "OneHotEncoderTransform":

etna/transforms/math/add_constant.py

@@ -136,7 +136,7 @@ def _inverse_transform(self, df: pd.DataFrame) -> pd.DataFrame:
     def get_regressors_info(self) -> List[str]:
         """Return the list with regressors created by the transform."""
         if self.in_column_regressor is None:
-            warnings.warn("Regressors info might be incorrect. Fit the transform to get the correct regressors info.")
+            raise ValueError("Fit the transform to get the correct regressors info!")
         return [self._get_column_name()] if self.in_column_regressor and not self.inplace else []
 
 

etna/transforms/math/apply_lambda.py

@@ -154,5 +154,5 @@ def _inverse_transform(self, df: pd.DataFrame) -> pd.DataFrame:
     def get_regressors_info(self) -> List[str]:
         """Return the list with regressors created by the transform."""
         if self.in_column_regressor is None:
-            warnings.warn("Regressors info might be incorrect. Fit the transform to get the correct regressors info.")
+            raise ValueError("Fit the transform to get the correct regressors info!")
         return [self.change_column] if self.in_column_regressor and not self.inplace else []

etna/transforms/math/differencing.py

@@ -1,4 +1,3 @@
-import warnings
 from typing import Dict
 from typing import List
 from typing import Optional
@@ -84,7 +83,7 @@ def get_regressors_info(self) -> List[str]:
         if self.inplace:
             return []
         if self.in_column_regressor is None:
-            warnings.warn("Regressors info might be incorrect. Fit the transform to get the correct regressors info.")
+            raise ValueError("Fit the transform to get the correct regressors info!")
         return [self._get_column_name()] if self.in_column_regressor else []
 
     def fit(self, ts: TSDataset) -> "_SingleDifferencingTransform":
@@ -362,7 +361,7 @@ def get_regressors_info(self) -> List[str]:
         if self.inplace:
             return []
         if self.in_column_regressor is None:
-            warnings.warn("Regressors info might be incorrect. Fit the transform to get the correct regressors info.")
+            raise ValueError("Fit the transform to get the correct regressors info!")
         return [self._get_column_name()] if self.in_column_regressor else []
 
     def fit(self, ts: TSDataset) -> "DifferencingTransform":

etna/transforms/math/log.py

@@ -139,7 +139,7 @@ def _inverse_transform(self, df: pd.DataFrame) -> pd.DataFrame:
     def get_regressors_info(self) -> List[str]:
         """Return the list with regressors created by the transform."""
         if self.in_column_regressor is None:
-            warnings.warn("Regressors info might be incorrect. Fit the transform to get the correct regressors info.")
+            raise ValueError("Fit the transform to get the correct regressors info!")
         return [self._get_column_name()] if self.in_column_regressor and not self.inplace else []
 
 

etna/transforms/math/sklearn.py

@@ -254,6 +254,5 @@ def get_regressors_info(self) -> List[str]:
         if self.inplace:
             return []
         if self.out_column_regressors is None:
-            warnings.warn("Regressors info might be incorrect. Fit the transform to get the correct regressors info.")
-            return []
+            raise ValueError("Fit the transform to get the correct regressors info!")
         return self.out_column_regressors

etna/transforms/math/statistics.py

@@ -1,4 +1,3 @@
-import warnings
 from abc import ABC
 from abc import abstractmethod
 from typing import List
@@ -110,7 +109,7 @@ def _transform(self, df: pd.DataFrame) -> pd.DataFrame:
     def get_regressors_info(self) -> List[str]:
         """Return the list with regressors created by the transform."""
         if self.in_column_regressor is None:
-            warnings.warn("Regressors info might be incorrect. Fit the transform to get the correct regressors info.")
+            raise ValueError("Fit the transform to get the correct regressors info!")
         return [self.out_column_name] if self.in_column_regressor else []
 
 

etna/transforms/missing_values/resample.py

@@ -167,7 +167,7 @@ def get_regressors_info(self) -> List[str]:
         if self.inplace:
             return []
         if self.in_column_regressor is None:
-            warnings.warn("Regressors info might be incorrect. Fit the transform to get the correct regressors info.")
+            raise ValueError("Fit the transform to get the correct regressors info!")
         return [self.out_column] if self.in_column_regressor else []
 
     def fit(self, ts: TSDataset) -> "ResampleWithDistributionTransform":