[MNT] Testing fixes

aeon/testing/estimator_checking/_yield_estimator_checks.py

@@ -627,10 +627,9 @@ def check_persistence_via_pickle(estimator, datatype):
             same, msg = deep_equals(output, results[i], return_msg=True)
             if not same:
                 raise ValueError(
-                    f"Running {method} after serialisation parameters gives "
-                    f"different results. "
-                    f"{type(estimator)} returns data as {type(output)}: test "
-                    f"equivalence message: {msg}"
+                    f"Running {type(estimator)} {method} with test parameters after "
+                    f"serialisation gives different results. "
+                    f"Check equivalence message: {msg}"
                 )
             i += 1
 
@@ -657,9 +656,8 @@ def check_fit_deterministic(estimator, datatype):
             same, msg = deep_equals(output, results[i], return_msg=True)
             if not same:
                 raise ValueError(
-                    f"Running {method} with test parameters after two calls to fit "
-                    f"gives different results."
-                    f"{type(estimator)} returns data as {type(output)}: test "
-                    f"equivalence message: {msg}"
+                    f"Running {type(estimator)} {method} with test parameters after "
+                    f"two calls to fit gives different results."
+                    f"Check equivalence message: {msg}"
                 )
             i += 1

aeon/testing/testing_config.py

@@ -23,8 +23,10 @@
 NUMBA_DISABLED = os.environ.get("NUMBA_DISABLE_JIT") == "1"
 
 # exclude estimators here for short term fixes
-# Hydra excluded because it returns a pytorch Tensor
-EXCLUDE_ESTIMATORS = ["REDCOMETS", "HydraTransformer"]
+EXCLUDE_ESTIMATORS = [
+    "REDCOMETS",
+    "HydraTransformer",  # returns a pytorch Tensor
+]
 
 # Exclude specific tests for estimators here
 EXCLUDED_TESTS = {
@@ -50,6 +52,7 @@
     "RSASTClassifier": ["check_fit_deterministic"],
     "SAST": ["check_fit_deterministic"],
     "RSAST": ["check_fit_deterministic"],
+    "MatrixProfile": ["check_persistence_via_pickle"],
     # missed in legacy testing, changes state in predict/transform
     "FLUSSSegmenter": ["check_non_state_changing_method"],
     "InformationGainSegmenter": ["check_non_state_changing_method"],

aeon/testing/utils/deep_equals.py

@@ -56,7 +56,7 @@ def _deep_equals(x, y, depth, ignore_index):
     elif isinstance(x, pd.DataFrame):
         return _dataframe_equals(x, y, depth, ignore_index)
     elif isinstance(x, np.ndarray):
-        return _numpy_equals(x, y, depth)
+        return _numpy_equals(x, y, depth, ignore_index)
     elif isinstance(x, (list, tuple)):
         return _list_equals(x, y, depth, ignore_index)
     elif isinstance(x, dict):
@@ -128,15 +128,21 @@ def _dataframe_equals(x, y, depth, ignore_index):
         return eq, msg
 
 
-def _numpy_equals(x, y, depth):
+def _numpy_equals(x, y, depth, ignore_index):
     if x.dtype != y.dtype:
         return False, f"x.dtype ({x.dtype}) != y.dtype ({y.dtype})"
+
     if x.dtype == "object":
-        eq, msg = _deep_equals(x.tolist(), y.tolist(), depth, ignore_index=True)
+        for i in range(len(x)):
+            eq, msg = _deep_equals(x[i], y[i], depth + 1, ignore_index)
+
+            if not eq:
+                return False, msg + f", idx={i}"
     else:
         eq = np.allclose(x, y, equal_nan=True)
         msg = "" if eq else f"x ({x}) != y ({y}), depth={depth}"
-    return eq, msg
+        return eq, msg
+    return True, ""
 
 
 def _csrmatrix_equals(x, y, depth):

pyproject.toml

@@ -176,6 +176,7 @@ addopts = '''
     --dist worksteal
     --reruns 2
     --only-rerun "crashed while running"
+    --only-rerun "zipfile.BadZipFile"
 '''
 filterwarnings = '''
     ignore::UserWarning