Unlock experimental tests (#464)

.github/workflows/test.yml

@@ -73,7 +73,7 @@ jobs:
 
       - name: PyTest with sharding
         run: |
-          poetry run pytest tests -v --shard-id=${{ matrix.shard-id }} --num-shards=3 --cov=etna --ignore=tests/test_experimental --cov-report=xml --durations=10
+          poetry run pytest tests -v --shard-id=${{ matrix.shard-id }} --num-shards=3 --cov=etna --cov-report=xml --durations=10
           poetry run pytest etna -v --doctest-modules --ignore=etna/libs --durations=10
 
       - name: Upload coverage

etna/pipeline/autoregressive_pipeline.py

@@ -139,6 +139,7 @@ def _forecast(self, ts: TSDataset, return_components: bool) -> TSDataset:
                 freq=ts.freq,
                 df_exog=ts.df_exog,
                 known_future=ts.known_future,
+                hierarchical_structure=ts.hierarchical_structure,
             )
             with warnings.catch_warnings():
                 warnings.filterwarnings(
@@ -171,7 +172,13 @@ def _forecast(self, ts: TSDataset, return_components: bool) -> TSDataset:
             prediction_df = prediction_df.combine_first(current_ts_future.to_pandas()[prediction_df.columns])
 
         # construct dataset and add all features
-        prediction_ts = TSDataset(df=prediction_df, freq=ts.freq, df_exog=ts.df_exog, known_future=ts.known_future)
+        prediction_ts = TSDataset(
+            df=prediction_df,
+            freq=ts.freq,
+            df_exog=ts.df_exog,
+            known_future=ts.known_future,
+            hierarchical_structure=ts.hierarchical_structure,
+        )
         prediction_ts.transform(self.transforms)
         prediction_ts.inverse_transform(self.transforms)
 

tests/test_experimental/test_prediction_intervals/test_base.py

@@ -208,24 +208,16 @@ def test_params_to_tune(pipeline, expected_params_to_tune):
             horizon=1,
             reconciliator=BottomUpReconciliator(target_level="market", source_level="product"),
         ),
+        DirectEnsemble(pipelines=[get_naive_pipeline(horizon=1), get_naive_pipeline_with_transforms(horizon=2)]),
+        VotingEnsemble(pipelines=[get_naive_pipeline(horizon=1), get_naive_pipeline_with_transforms(horizon=1)]),
+        StackingEnsemble(pipelines=[get_naive_pipeline(horizon=1), get_naive_pipeline_with_transforms(horizon=1)]),
     ),
 )
 def test_valid_params_sampling(product_level_constant_hierarchical_ts, pipeline):
     intervals_pipeline = DummyPredictionIntervals(pipeline=pipeline)
     assert_sampling_is_valid(intervals_pipeline=intervals_pipeline, ts=product_level_constant_hierarchical_ts)
 
 
-@pytest.mark.parametrize(
-    "pipeline",
-    (VotingEnsemble(pipelines=[get_naive_pipeline(horizon=1), get_naive_pipeline_with_transforms(horizon=1)]),),
-)
-def test_default_params_to_tune_error(pipeline):
-    intervals_pipeline = DummyPredictionIntervals(pipeline=pipeline)
-
-    with pytest.raises(NotImplementedError, match=f"{pipeline.__class__.__name__} doesn't support"):
-        _ = intervals_pipeline.params_to_tune()
-
-
 @pytest.mark.parametrize("load_ts", (True, False))
 @pytest.mark.parametrize(
     "pipeline",