[MNT] Add/rework transformation tests and remove from exclude list

aeon/testing/estimator_checking/_estimator_checking.py

@@ -18,7 +18,12 @@
 from aeon.testing.estimator_checking._yield_estimator_checks import (
     _yield_all_aeon_checks,
 )
-from aeon.testing.testing_config import EXCLUDE_ESTIMATORS, EXCLUDED_TESTS
+from aeon.testing.testing_config import (
+    EXCLUDE_ESTIMATORS,
+    EXCLUDED_TESTS,
+    EXCLUDED_TESTS_NO_NUMBA,
+    NUMBA_DISABLED,
+)
 from aeon.utils.validation._dependencies import (
     _check_estimator_deps,
     _check_soft_dependencies,
@@ -313,6 +318,8 @@ def _should_be_skipped(estimator, check, has_dependencies):
         return True, "In aeon estimator exclude list", check_name
     elif check_name in EXCLUDED_TESTS.get(est_name, []):
         return True, "In aeon test exclude list for estimator", check_name
+    elif NUMBA_DISABLED and check_name in EXCLUDED_TESTS_NO_NUMBA.get(est_name, []):
+        return True, "In aeon no numba test exclude list for estimator", check_name
 
     return False, "", check_name
 

aeon/testing/estimator_checking/_yield_classification_checks.py

@@ -41,19 +41,14 @@ def _yield_classification_checks(estimator_class, estimator_instances, datatypes
             results_dict=unit_test_proba,
             resample_seed=0,
         )
-        # the test currently fails when numba is disabled. See issue #622
-        if (
-            estimator_class.__name__ != "HIVECOTEV2"
-            or os.environ.get("NUMBA_DISABLE_JIT") != "1"
-        ):
-            yield partial(
-                check_classifier_against_expected_results,
-                estimator_class=estimator_class,
-                data_name="BasicMotions",
-                data_loader=load_basic_motions,
-                results_dict=basic_motions_proba,
-                resample_seed=4,
-            )
+        yield partial(
+            check_classifier_against_expected_results,
+            estimator_class=estimator_class,
+            data_name="BasicMotions",
+            data_loader=load_basic_motions,
+            results_dict=basic_motions_proba,
+            resample_seed=4,
+        )
     yield partial(check_classifier_overrides_and_tags, estimator_class=estimator_class)
 
     # data type irrelevant
@@ -154,9 +149,6 @@ def check_classifier_overrides_and_tags(estimator_class):
                 f"Override _{method} instead."
             )
 
-    # axis class parameter is for internal use only
-    assert "axis" not in estimator_class.__dict__
-
     # Test valid tag for X_inner_type
     X_inner_type = estimator_class.get_class_tag(tag_name="X_inner_type")
     if isinstance(X_inner_type, str):
@@ -172,11 +164,6 @@ def check_classifier_overrides_and_tags(estimator_class):
         else:  # must be a list
             assert any([t in valid_unequal_types for t in X_inner_type])
 
-    # Must have at least one set to True
-    multi = estimator_class.get_class_tag(tag_name="capability:multivariate")
-    uni = estimator_class.get_class_tag(tag_name="capability:univariate")
-    assert multi or uni
-
     valid_algorithm_types = [
         "distance",
         "deeplearning",

aeon/testing/estimator_checking/_yield_estimator_checks.py

@@ -26,7 +26,6 @@
 from aeon.regression import BaseRegressor
 from aeon.regression.deep_learning.base import BaseDeepRegressor
 from aeon.segmentation import BaseSegmenter
-from aeon.similarity_search import BaseSimilaritySearch
 from aeon.testing.estimator_checking._yield_anomaly_detection_checks import (
     _yield_anomaly_detection_checks,
 )
@@ -36,9 +35,6 @@
 from aeon.testing.estimator_checking._yield_clustering_checks import (
     _yield_clustering_checks,
 )
-from aeon.testing.estimator_checking._yield_collection_transformation_checks import (
-    _yield_collection_transformation_checks,
-)
 from aeon.testing.estimator_checking._yield_early_classification_checks import (
     _yield_early_classification_checks,
 )
@@ -48,12 +44,6 @@
 from aeon.testing.estimator_checking._yield_segmentation_checks import (
     _yield_segmentation_checks,
 )
-from aeon.testing.estimator_checking._yield_series_transformation_checks import (
-    _yield_series_transformation_checks,
-)
-from aeon.testing.estimator_checking._yield_similarity_search_checks import (
-    _yield_similarity_search_checks,
-)
 from aeon.testing.estimator_checking._yield_soft_dependency_checks import (
     _yield_soft_dependency_checks,
 )
@@ -68,8 +58,6 @@
 from aeon.testing.utils.deep_equals import deep_equals
 from aeon.testing.utils.estimator_checks import _get_tag, _run_estimator_method
 from aeon.transformations.base import BaseTransformer
-from aeon.transformations.collection import BaseCollectionTransformer
-from aeon.transformations.series import BaseSeriesTransformer
 from aeon.utils.base import VALID_ESTIMATOR_BASES
 from aeon.utils.tags import check_valid_tags
 from aeon.utils.validation._dependencies import _check_estimator_deps
@@ -148,26 +136,11 @@ def _yield_all_aeon_checks(
             estimator_class, estimator_instances, datatypes
         )
 
-    if issubclass(estimator_class, BaseSimilaritySearch):
-        yield from _yield_similarity_search_checks(
-            estimator_class, estimator_instances, datatypes
-        )
-
     if issubclass(estimator_class, BaseTransformer):
         yield from _yield_transformation_checks(
             estimator_class, estimator_instances, datatypes
         )
 
-    if issubclass(estimator_class, BaseCollectionTransformer):
-        yield from _yield_collection_transformation_checks(
-            estimator_class, estimator_instances, datatypes
-        )
-
-    if issubclass(estimator_class, BaseSeriesTransformer):
-        yield from _yield_series_transformation_checks(
-            estimator_class, estimator_instances, datatypes
-        )
-
 
 def _yield_estimator_checks(estimator_class, estimator_instances, datatypes):
     """Yield all general checks for an aeon estimator."""
@@ -299,6 +272,14 @@ def check_has_common_interface(estimator_class):
         estimator_class.get_fitted_params
     )
 
+    # axis class parameter is for internal use only
+    assert "axis" not in estimator_class.__dict__
+
+    # Must have at least one set to True
+    multi = estimator_class.get_class_tag(tag_name="capability:multivariate")
+    uni = estimator_class.get_class_tag(tag_name="capability:univariate")
+    assert multi or uni
+
 
 def check_set_params_sklearn(estimator_class):
     """Check that set_params works correctly, mirrors sklearn check_set_params.

aeon/testing/estimator_checking/_yield_regression_checks.py

@@ -145,9 +145,6 @@ def check_regressor_overrides_and_tags(estimator_class):
                 f"Override _{method} instead."
             )
 
-    # axis class parameter is for internal use only
-    assert "axis" not in estimator_class.__dict__
-
     # Test valid tag for X_inner_type
     X_inner_type = estimator_class.get_class_tag(tag_name="X_inner_type")
     if isinstance(X_inner_type, str):
@@ -163,11 +160,6 @@ def check_regressor_overrides_and_tags(estimator_class):
         else:  # must be a list
             assert any([t in valid_unequal_types for t in X_inner_type])
 
-    # Must have at least one set to True
-    multi = estimator_class.get_class_tag(tag_name="capability:multivariate")
-    uni = estimator_class.get_class_tag(tag_name="capability:univariate")
-    assert multi or uni
-
     valid_algorithm_types = [
         "distance",
         "deeplearning",