updated tests

etna/transforms/decomposition/model_based.py

@@ -135,7 +135,7 @@ def transform(self, ts: TSDataset) -> TSDataset:
 
         if ts.index.min() < self._first_timestamp:
             raise ValueError(
-                f"First index of the dataset to be transformed must be larger or equal then {self._first_timestamp}!"
+                f"First index of the dataset to be transformed must be larger or equal than {self._first_timestamp}!"
             )
 
         if ts.index.min() > self._last_timestamp:

tests/test_transforms/test_decomposition/conftest.py

@@ -0,0 +1,34 @@
+import numpy as np
+import pytest
+
+from etna.datasets import TSDataset
+from etna.datasets import generate_ar_df
+
+
+@pytest.fixture()
+def ts_with_exogs() -> TSDataset:
+    periods = 100
+    periods_exog = periods + 10
+    df = generate_ar_df(start_time="2020-01-01", periods=periods, freq="D", n_segments=2)
+    df_exog = generate_ar_df(start_time="2020-01-01", periods=periods_exog, freq="D", n_segments=2, random_seed=2)
+    df_exog.rename(columns={"target": "exog"}, inplace=True)
+    df_exog["holiday"] = np.random.choice([0, 1], size=periods_exog * 2)
+
+    ts = TSDataset(df, freq="D", df_exog=df_exog, known_future="all")
+    return ts
+
+
+@pytest.fixture()
+def ts_with_exogs_train_test(ts_with_exogs):
+    return ts_with_exogs.train_test_split(test_size=20)
+
+
+@pytest.fixture()
+def forward_stride_datasets(ts_with_exogs):
+    train_df = ts_with_exogs.df.iloc[:-10]
+    test_df = ts_with_exogs.df.iloc[-20:]
+
+    train_ts = TSDataset(df=train_df, freq=ts_with_exogs.freq)
+    test_ts = TSDataset(df=test_df, freq=ts_with_exogs.freq)
+
+    return train_ts, test_ts

tests/test_transforms/test_decomposition/test_dft_based.py

@@ -3,7 +3,6 @@
 import pytest
 
 from etna.datasets import TSDataset
-from etna.datasets import generate_ar_df
 from etna.metrics import MAE
 from etna.models import CatBoostPerSegmentModel
 from etna.models import HoltWintersModel
@@ -23,35 +22,6 @@ def simple_pipeline_with_decompose(in_column, horizon, k):
     return pipeline
 
 
-@pytest.fixture()
-def ts_with_exogs() -> TSDataset:
-    periods = 100
-    periods_exog = periods + 10
-    df = generate_ar_df(start_time="2020-01-01", periods=periods, freq="D", n_segments=2)
-    df_exog = generate_ar_df(start_time="2020-01-01", periods=periods_exog, freq="D", n_segments=2, random_seed=2)
-    df_exog.rename(columns={"target": "exog"}, inplace=True)
-    df_exog["holiday"] = np.random.choice([0, 1], size=periods_exog * 2)
-
-    ts = TSDataset(df, freq="D", df_exog=df_exog, known_future="all")
-    return ts
-
-
-@pytest.fixture()
-def ts_with_exogs_train_test(ts_with_exogs):
-    return ts_with_exogs.train_test_split(test_size=20)
-
-
-@pytest.fixture()
-def forward_stride_datasets(ts_with_exogs):
-    train_df = ts_with_exogs.df.iloc[:-10]
-    test_df = ts_with_exogs.df.iloc[-20:]
-
-    train_ts = TSDataset(df=train_df, freq=ts_with_exogs.freq)
-    test_ts = TSDataset(df=test_df, freq=ts_with_exogs.freq)
-
-    return train_ts, test_ts
-
-
 @pytest.fixture()
 def ts_with_missing(ts_with_exogs):
     target_df = ts_with_exogs[..., "target"]

tests/test_transforms/test_decomposition/test_model_based.py

@@ -2,8 +2,6 @@
 import pandas as pd
 import pytest
 
-from etna.datasets import TSDataset
-from etna.datasets import generate_ar_df
 from etna.metrics import MAE
 from etna.models import BATSModel
 from etna.models import CatBoostPerSegmentModel
@@ -28,37 +26,6 @@ def simple_pipeline_with_decompose(in_column, horizon):
     return pipeline
 
 
-@pytest.fixture()
-def ts_with_exogs() -> TSDataset:
-    periods = 100
-    periods_exog = periods + 10
-    df = generate_ar_df(start_time="2020-01-01", periods=periods, freq="D", n_segments=2)
-    df_exog = generate_ar_df(start_time="2020-01-01", periods=periods_exog, freq="D", n_segments=2, random_seed=2)
-    df_exog.rename(columns={"target": "exog"}, inplace=True)
-    df_exog["holiday"] = np.random.choice([0, 1], size=periods_exog * 2)
-
-    df = TSDataset.to_dataset(df)
-    df_exog = TSDataset.to_dataset(df_exog)
-    ts = TSDataset(df, freq="D", df_exog=df_exog, known_future="all")
-    return ts
-
-
-@pytest.fixture()
-def ts_with_exogs_train_test(ts_with_exogs):
-    return ts_with_exogs.train_test_split(test_size=20)
-
-
-@pytest.fixture()
-def forward_stride_datasets(ts_with_exogs):
-    train_df = ts_with_exogs.df.iloc[:-10]
-    test_df = ts_with_exogs.df.iloc[-20:]
-
-    train_ts = TSDataset(df=train_df, freq=ts_with_exogs.freq)
-    test_ts = TSDataset(df=test_df, freq=ts_with_exogs.freq)
-
-    return train_ts, test_ts
-
-
 @pytest.mark.parametrize("in_column", ("target", "feat"))
 def test_init(in_column):
     transform = ModelDecomposeTransform(model=HoltWintersModel(), in_column=in_column)

tests/test_transforms/test_inference/test_inverse_transform.py

@@ -3015,6 +3015,7 @@ def test_inverse_transform_future_with_target_fail_difference(
         "transform, dataset_name, expected_changes",
         [
             (FourierDecomposeTransform(in_column="target", k=5, residuals=True), "regular_ts", {}),
+            (ModelDecomposeTransform(model=HoltWintersModel(), in_column="target", residuals=True), "regular_ts", {}),
         ],
     )
     def test_inverse_transform_future_with_target_fail_require_history(

tests/test_transforms/test_inference/test_transform.py

@@ -2855,6 +2855,11 @@ def test_transform_future_with_target(self, transform, dataset_name, expected_ch
                 "regular_ts",
                 {"create": {"target_dft_0", "target_dft_1", "target_dft_residuals"}},
             ),
+            (
+                ModelDecomposeTransform(model=HoltWintersModel(), in_column="target", residuals=True),
+                "regular_ts",
+                {"create": {"target_level", "target_residuals"}},
+            ),
         ),
     )
     def test_transform_future_with_target_fail_require_history(