fixes after rebase

Signed-off-by: Mateusz Switala <mswitala@redhat.com>
Assisted-by: Claude Code

Author: Mateusz-Switala

components/training/automl/autogluon_timeseries_models_training/component.py

@@ -80,15 +80,15 @@ def autogluon_timeseries_models_training(
     logger = logging.getLogger(__name__)
 
     from kfp_components.components.training.automl.shared.back_testing import build_back_testing_json
-    from kfp_components.components.training.automl.shared.component_status import ComponentStatusTracker
-    from kfp_components.components.training.automl.shared.run_status import shared_automl_dir
     from kfp_components.components.training.automl.shared.timeseries_notebook_utils import (
         build_predict_sample_artifact,
     )
 
     status = ComponentStatusTracker(component_status.path, "autogluon_timeseries_models_training")
     with status:
         TOP_N_MAX = 7
+        DEFAULT_PRESETS = "fast_training"
+        DEFAULT_TIME_LIMIT = 600  # 10 minutes
 
         # Input validation
         for param, value in (

components/training/automl/autogluon_timeseries_models_training/tests/test_component_unit.py

@@ -650,7 +650,8 @@ def test_custom_eval_metric_passed_to_predictors_and_returned(
         mock_refit_predictor.evaluate.return_value = {"WQL": 0.3, "MASE": 0.5}
 
         mock_predictor_cls.side_effect = [mock_predictor, mock_refit_predictor]
-        mock_ts_df_cls.from_data_frame.side_effect = [_mock_ts_df(), _mock_ts_df()]
+        # from_data_frame: train, test, and once per model in build_predict_sample_artifact (1 model)
+        mock_ts_df_cls.from_data_frame.side_effect = [_mock_ts_df(), _mock_ts_df(), _mock_ts_df()]
         mock_ts_df_cls.from_path.return_value = _mock_ts_df()
         mock_ts_df_cls.return_value = _mock_ts_df()
         mock_concat.return_value = mock.MagicMock()