equal nans

aeon/testing/estimator_checking/_yield_estimator_checks.py

@@ -608,7 +608,7 @@ def check_raises_not_fitted_error(estimator, datatype):
 def _equal_outputs(output1, output2):
     """Test whether two outputs from an estimator are logically identical.
 
-    Valid data strutures are:
+    Valid data structures are:
     1. numpy array: stores an equal length collection or series
     2. dict: a histogram of counts, usually of discrete series
     3. pd.DataFrame: series stored in dataframe
@@ -619,7 +619,7 @@ def _equal_outputs(output1, output2):
     if type(output1) is not type(output2):
         return False
     if isinstance(output1, np.ndarray):  # 1. X an equal length collection or series
-        return np.allclose(output1, output2)
+        return np.allclose(output1, output2, equal_nan=True)
     if isinstance(output1, dict):  # 2. X a dictionary, dense collection or series
         if output1.keys() != output2.keys():
             return False
@@ -629,7 +629,7 @@ def _equal_outputs(output1, output2):
         return True
     if isinstance(output1, pd.DataFrame) or isinstance(output1, pd.Series):
         # 3. X a dataframe
-        return np.allclose(output1.values, output2.values)
+        return np.allclose(output1.values, output2.values, equal_nan=True)
     if isinstance(output1, list):  # X a possibly unequal length collection
         if len(output1) != len(output2):
             return False
@@ -670,7 +670,8 @@ def check_persistence_via_pickle(estimator, datatype):
             if not _equal_outputs(output, results[i]):
                 raise ValueError(
                     f"Running {method} after serialisation parameters gives "
-                    f"different results."
+                    f"different results. "
+                    f"{type(estimator)} returns data as {type(output)}"
                 )
             i += 1
 
@@ -696,7 +697,8 @@ def check_fit_deterministic(estimator, datatype):
             output = _run_estimator_method(estimator, method, datatype, "test")
             if not _equal_outputs(output, results[i]):
                 raise ValueError(
-                    f"Running {method} after fit twice with test "
-                    f"parameters gives different results."
+                    f"Running {method} with test parameters after two calls to fit "
+                    f"gives different results."
+                    f"{type(estimator)} returns data as {type(output)}"
                 )
             i += 1