revert non-objdet changes

Author: czaloom

src/valor_lite/classification/manager.py

@@ -16,7 +16,7 @@
     unpack_confusion_matrix_into_metric_list,
     unpack_precision_recall_rocauc_into_metric_lists,
 )
-from valor_lite.exceptions import EmptyCacheError, EmptyFilterError
+from valor_lite.exceptions import EmptyEvaluatorError, EmptyFilterError
 
 """
 Usage
@@ -515,7 +515,7 @@ def finalize(self):
             A ready-to-use evaluator object.
         """
         if self._detailed_pairs.size == 0:
-            raise EmptyCacheError()
+            raise EmptyEvaluatorError()
 
         self._label_metadata = compute_label_metadata(
             ids=self._detailed_pairs[:, :3].astype(np.int32),

src/valor_lite/semantic_segmentation/manager.py

@@ -4,7 +4,7 @@
 from numpy.typing import NDArray
 from tqdm import tqdm
 
-from valor_lite.exceptions import EmptyCacheError, EmptyFilterError
+from valor_lite.exceptions import EmptyEvaluatorError, EmptyFilterError
 from valor_lite.semantic_segmentation.annotation import Segmentation
 from valor_lite.semantic_segmentation.computation import (
     compute_intermediate_confusion_matrices,
@@ -426,7 +426,7 @@ def finalize(self) -> Evaluator:
         """
 
         if len(self.matrices) == 0:
-            raise EmptyCacheError()
+            raise EmptyEvaluatorError()
 
         n_labels = len(self._evaluator.index_to_label)
         n_datums = len(self._evaluator.index_to_datum_id)

tests/classification/test_accuracy.py

@@ -67,6 +67,8 @@ def test_accuracy_basic(basic_classifications: list[Classification]):
     loader.add_data(basic_classifications)
     evaluator = loader.finalize()
 
+    assert evaluator.ignored_prediction_labels == ["1", "2"]
+    assert evaluator.missing_prediction_labels == []
     assert evaluator.metadata.to_dict() == {
         "number_of_datums": 3,
         "number_of_ground_truths": 3,
@@ -162,6 +164,8 @@ def test_accuracy_with_image_example(
     loader.add_data(classifications_image_example)
     evaluator = loader.finalize()
 
+    assert evaluator.ignored_prediction_labels == ["v1", "v8", "v5"]
+    assert evaluator.missing_prediction_labels == []
     assert evaluator.metadata.to_dict() == {
         "number_of_datums": 2,
         "number_of_ground_truths": 2,
@@ -195,6 +199,8 @@ def test_accuracy_with_tabular_example(
     loader.add_data(classifications_tabular_example)
     evaluator = loader.finalize()
 
+    assert evaluator.ignored_prediction_labels == []
+    assert evaluator.missing_prediction_labels == []
     assert evaluator.metadata.to_dict() == {
         "number_of_datums": 10,
         "number_of_ground_truths": 10,

tests/classification/test_dataloader.py

@@ -1,12 +1,12 @@
 import pytest
 
 from valor_lite.classification import Classification, DataLoader
-from valor_lite.exceptions import EmptyCacheError
+from valor_lite.exceptions import EmptyEvaluatorError
 
 
 def test_finalization_no_data():
     loader = DataLoader()
-    with pytest.raises(EmptyCacheError):
+    with pytest.raises(EmptyEvaluatorError):
         loader.finalize()
 
 

tests/semantic_segmentation/test_dataloader.py

@@ -1,12 +1,12 @@
 import pytest
 
-from valor_lite.exceptions import EmptyCacheError
+from valor_lite.exceptions import EmptyEvaluatorError
 from valor_lite.semantic_segmentation import DataLoader, Segmentation
 
 
 def test_no_data():
     loader = DataLoader()
-    with pytest.raises(EmptyCacheError):
+    with pytest.raises(EmptyEvaluatorError):
         loader.finalize()
 
 

tests/semantic_segmentation/test_evaluator.py

@@ -113,6 +113,7 @@ def test_label_mismatch():
         ]
     )
     evaluator = loader.finalize()
+    print(evaluator._confusion_matrices)
     assert np.all(
         evaluator._confusion_matrices
         == np.array(
@@ -171,6 +172,7 @@ def test_empty_groundtruths():
         ]
     )
     evaluator = loader.finalize()
+    print(evaluator._confusion_matrices)
     assert np.all(
         evaluator._confusion_matrices
         == np.array(
@@ -227,6 +229,7 @@ def test_empty_predictions():
         ]
     )
     evaluator = loader.finalize()
+    print(evaluator._confusion_matrices)
     assert np.all(
         evaluator._confusion_matrices
         == np.array(