Add tests

etna/transforms/math/differencing.py

@@ -80,10 +80,10 @@ def _get_column_name(self) -> str:
 
     def get_regressors_info(self) -> List[str]:
         """Return the list with regressors created by the transform."""
-        if self.inplace:
-            return []
         if self.in_column_regressor is None:
             raise ValueError("Fit the transform to get the correct regressors info!")
+        if self.inplace:
+            return []
         return [self._get_column_name()] if self.in_column_regressor else []
 
     def fit(self, ts: TSDataset) -> "_SingleDifferencingTransform":
@@ -358,10 +358,10 @@ def _get_column_name(self) -> str:
 
     def get_regressors_info(self) -> List[str]:
         """Return the list with regressors created by the transform."""
-        if self.inplace:
-            return []
         if self.in_column_regressor is None:
             raise ValueError("Fit the transform to get the correct regressors info!")
+        if self.inplace:
+            return []
         return [self._get_column_name()] if self.in_column_regressor else []
 
     def fit(self, ts: TSDataset) -> "DifferencingTransform":

etna/transforms/math/sklearn.py

@@ -251,8 +251,8 @@ def _inverse_reshape(self, df: pd.DataFrame, transformed: np.ndarray) -> np.ndar
 
     def get_regressors_info(self) -> List[str]:
         """Return the list with regressors created by the transform."""
-        if self.inplace:
-            return []
         if self.out_column_regressors is None:
             raise ValueError("Fit the transform to get the correct regressors info!")
+        if self.inplace:
+            return []
         return self.out_column_regressors

etna/transforms/missing_values/resample.py

@@ -164,10 +164,10 @@ def _get_out_column(self, out_column: Optional[str]) -> str:
 
     def get_regressors_info(self) -> List[str]:
         """Return the list with regressors created by the transform."""
-        if self.inplace:
-            return []
         if self.in_column_regressor is None:
             raise ValueError("Fit the transform to get the correct regressors info!")
+        if self.inplace:
+            return []
         return [self.out_column] if self.in_column_regressor else []
 
     def fit(self, ts: TSDataset) -> "ResampleWithDistributionTransform":

tests/test_transforms/test_encoders/test_categorical_transform.py

@@ -342,3 +342,11 @@ def test_save_load_ohe(dtype):
     for i in range(3):
         transform = OneHotEncoderTransform(in_column=f"regressor_{i}", out_column="test")
         assert_transformation_equals_loaded_original(transform=transform, ts=ts)
+
+
+@pytest.mark.parametrize(
+    "transform", (OneHotEncoderTransform(in_column="regressor_0"), LabelEncoderTransform(in_column="regressor_0"))
+)
+def test_get_regressors_info_not_fitted(transform):
+    with pytest.raises(ValueError, match="Fit the transform to get the correct regressors info!"):
+        _ = transform.get_regressors_info()

tests/test_transforms/test_math/test_add_constant_transform.py

@@ -80,3 +80,9 @@ def test_fit_transform_with_nans(ts_diff_endings):
 def test_save_load(inplace, example_tsds):
     transform = AddConstTransform(in_column="target", value=10, inplace=inplace)
     assert_transformation_equals_loaded_original(transform=transform, ts=example_tsds)
+
+
+def test_get_regressors_info_not_fitted():
+    transform = AddConstTransform(in_column="target", value=1, out_column="out_column")
+    with pytest.raises(ValueError, match="Fit the transform to get the correct regressors info!"):
+        _ = transform.get_regressors_info()

tests/test_transforms/test_math/test_differencing_transform.py

@@ -474,3 +474,9 @@ def test_save_load(inplace, ts_nans):
     ts = ts_nans
     transform = DifferencingTransform(in_column="target", inplace=inplace)
     assert_transformation_equals_loaded_original(transform=transform, ts=ts)
+
+
+def test_get_regressors_info_not_fitted():
+    transform = DifferencingTransform(in_column="target")
+    with pytest.raises(ValueError, match="Fit the transform to get the correct regressors info!"):
+        _ = transform.get_regressors_info()

tests/test_transforms/test_math/test_lambda_transform.py

@@ -158,3 +158,9 @@ def test_save_load(inplace, ts_range_const):
         inverse_transform_func=example_inverse_transform_func,
     )
     assert_transformation_equals_loaded_original(transform=transform, ts=ts_range_const)
+
+
+def test_get_regressors_info_not_fitted():
+    transform = LambdaTransform(in_column="target", inplace=False, transform_func=lambda x: x)
+    with pytest.raises(ValueError, match="Fit the transform to get the correct regressors info!"):
+        _ = transform.get_regressors_info()

tests/test_transforms/test_math/test_log_transform.py

@@ -118,3 +118,9 @@ def test_save_load(inplace, positive_ts_):
     ts = positive_ts_
     transform = LogTransform(in_column="target", inplace=inplace)
     assert_transformation_equals_loaded_original(transform=transform, ts=ts)
+
+
+def test_get_regressors_info_not_fitted():
+    transform = LogTransform(in_column="target")
+    with pytest.raises(ValueError, match="Fit the transform to get the correct regressors info!"):
+        _ = transform.get_regressors_info()

tests/test_transforms/test_math/test_sklearn_transform_interface.py

@@ -277,3 +277,23 @@ def test_ordering(transform_constructor, in_column, mode, multicolumn_ts):
         df_multi = transformed_df.loc[:, pd.IndexSlice[segments, column_multi]]
         df_single = transformed_dfs_one_column[i].loc[:, pd.IndexSlice[segments, column_single]]
         assert np.all(df_multi.values == df_single.values)
+
+
+@pytest.mark.parametrize(
+    "transform_constructor",
+    [
+        BoxCoxTransform,
+        YeoJohnsonTransform,
+        StandardScalerTransform,
+        RobustScalerTransform,
+        MinMaxScalerTransform,
+        MaxAbsScalerTransform,
+        StandardScalerTransform,
+        RobustScalerTransform,
+        MinMaxScalerTransform,
+    ],
+)
+def test_get_regressors_info_not_fitted(transform_constructor):
+    transform = transform_constructor(in_column="target")
+    with pytest.raises(ValueError, match="Fit the transform to get the correct regressors info!"):
+        _ = transform.get_regressors_info()

tests/test_transforms/test_math/test_statistics_transform.py

@@ -66,9 +66,7 @@ def test_get_regressors_info(example_reg_tsds, in_column, expected_regressors):
 
 def test_get_regressors_info_not_fitted():
     transform = DummyWindowStatisticsTransform(in_column="target", out_column="out_column", window=1)
-    with pytest.warns(
-        UserWarning, match="Regressors info might be incorrect. Fit the transform to get the correct regressors info."
-    ):
+    with pytest.raises(ValueError, match="Fit the transform to get the correct regressors info!"):
         _ = transform.get_regressors_info()
 
 

tests/test_transforms/test_missing_values/test_resample_transform.py

@@ -121,3 +121,9 @@ def test_save_load(inplace, out_column, daily_exog_ts):
         in_column="regressor_exog", inplace=inplace, distribution_column="target", out_column=out_column
     )
     assert_transformation_equals_loaded_original(transform=transform, ts=daily_exog_ts)
+
+
+def test_get_regressors_info_not_fitted():
+    transform = ResampleWithDistributionTransform(in_column="regressor_exog", distribution_column="target")
+    with pytest.raises(ValueError, match="Fit the transform to get the correct regressors info!"):
+        _ = transform.get_regressors_info()