don't return neutral value, refs #19

src/distance_explainer.py

@@ -3,10 +3,10 @@
 import logging
 import dianna.utils
 import numpy as np
+import numpy.typing
 from dianna.utils.maskers import generate_interpolated_float_masks_for_image
 from sklearn.metrics import pairwise_distances
 from tqdm import tqdm
-import numpy.typing
 
 logging.getLogger(__name__).addHandler(logging.NullHandler())
 
@@ -51,7 +51,8 @@ def __init__(self, n_masks=1000, feature_res=8, p_keep=.5,  # pylint: disable=to
         self.mask_selection_negative_range_min = mask_selection_negative_range_min
         self.batch_size = batch_size
 
-    def explain_image_distance(self, model_or_function, input_data, embedded_reference, masks=None) -> tuple[numpy.typing.NDArray, float]:
+    def explain_image_distance(self, model_or_function, input_data, embedded_reference, masks=None) -> tuple[
+        numpy.typing.NDArray, float]:
         """Explain an image with respect to a reference point in an embedded space.
 
         Args:
@@ -61,8 +62,7 @@ def explain_image_distance(self, model_or_function, input_data, embedded_referen
             masks: User specified masks, in case no autogenerated masks should be used.
 
         Returns:
-            saliency map and the neutral value within the saliency map which indicates the parts of the image that
-            neither bring the image closer nor further away from the embedded reference.
+            saliency map
         """
         full_preprocess_function, input_data = self._prepare_input_data(input_data)
         runner = dianna.utils.get_function(model_or_function, preprocess_function=full_preprocess_function)
@@ -72,7 +72,8 @@ def explain_image_distance(self, model_or_function, input_data, embedded_referen
         img_shape = input_data.shape[1:3]
         # Expose masks for to make user inspection possible
         if masks is None:
-            self.masks = generate_interpolated_float_masks_for_image(img_shape, active_p_keep, self.n_masks, self.feature_res)
+            self.masks = generate_interpolated_float_masks_for_image(img_shape, active_p_keep, self.n_masks,
+                                                                     self.feature_res)
         else:
             self.masks = masks
             if self.masks.shape[0] != self.n_masks:
@@ -124,17 +125,7 @@ def describe(x, name):
 
         saliency = unnormalized_sal
 
-        neutral_value = active_p_keep
-
-        # for one-sided experiments, use "meaningful" neutral value (the unperturbed distance), otherwise center on 0
-        if len(lowest_mask_weights) > 0 and len(highest_mask_weights) == 0:
-            neutral_value = neutral_value
-        if len(highest_mask_weights) > 0 and len(lowest_mask_weights) == 0:
-            neutral_value = -neutral_value
-        if len(highest_mask_weights) > 0 and len(lowest_mask_weights) > 0:
-            neutral_value = 0
-
-        return saliency, neutral_value
+        return saliency
 
     @staticmethod
     def _get_lowest_distance_masks_and_weights(embedded_reference, predictions, masks, mask_selection_range_min,

tests/test_distance.py

@@ -54,25 +54,12 @@ def test_distance_explainer_saliency(dummy_data: tuple[ArrayLike, ArrayLike],
     explainer = get_explainer(get_default_config())
     expected_saliency, expected_value = np.load('./tests/test_data/test_dummy_data_exact_expected_output.npz').values()
 
-    saliency, value = explainer.explain_image_distance(dummy_model, input_arr, embedded_reference)
+    saliency = explainer.explain_image_distance(dummy_model, input_arr, embedded_reference)
 
     assert saliency.shape == (1,) + input_arr.shape[:2] + (1,)  # Has correct shape
     assert np.allclose(expected_saliency, saliency)  # Has correct saliency
 
 
-@pytest.mark.skip("See 'neutral value not correct #19', https://github.com/dianna-ai/distance_explainer/issues/19")
-def test_distance_explainer_value(dummy_data: tuple[ArrayLike, ArrayLike],
-                                  dummy_model: Callable):
-    """Code output should be identical to recorded value."""
-    embedded_reference, input_arr = dummy_data
-    explainer = get_explainer(get_default_config())
-    expected_saliency, expected_value = np.load('./tests/test_data/test_dummy_data_exact_expected_output.npz').values()
-
-    saliency, value = explainer.explain_image_distance(dummy_model, input_arr, embedded_reference)
-
-    assert np.allclose(expected_value, value)  # Has correct value
-
-
 @pytest.mark.parametrize("empty_side,expected_tag",
                          [({"mask_selection_range_max": 0.}, "pos_empty"),
                           ({"mask_selection_negative_range_min": 1.}, "neg_empty")])
@@ -87,26 +74,6 @@ def test_distance_explainer_one_sided_saliency(dummy_data: tuple[ArrayLike, Arra
     config = dataclasses.replace(get_default_config(), **empty_side)
     explainer = get_explainer(config)
 
-    saliency, value = explainer.explain_image_distance(dummy_model, input_arr, embedded_reference)
+    saliency = explainer.explain_image_distance(dummy_model, input_arr, embedded_reference)
     assert saliency.shape == (1,) + input_arr.shape[:2] + (1,)  # Has correct shape
     assert np.allclose(expected_saliency, saliency)  # Has correct saliency
-
-
-@pytest.mark.skip("See 'neutral value not correct #19', https://github.com/dianna-ai/distance_explainer/issues/19")
-@pytest.mark.parametrize("empty_side,expected_tag",
-                         [({"mask_selection_range_max": 0.}, "pos_empty"),
-                          ({"mask_selection_negative_range_min": 1.}, "neg_empty")])
-def test_distance_explainer_one_sided_value(dummy_data: tuple[ArrayLike, ArrayLike],
-                                            dummy_model: Callable,
-                                            empty_side: dict[str, float],
-                                            expected_tag: str):
-    """Code output should be identical to recorded saliency."""
-    embedded_reference, input_arr = dummy_data
-    expected_saliency, expected_value = np.load(
-        f'./tests/test_data/test_dummy_data_exact_expected_output_{expected_tag}.npz').values()
-    config = dataclasses.replace(get_default_config(), **empty_side)
-    explainer = get_explainer(config)
-
-    saliency, value = explainer.explain_image_distance(dummy_model, input_arr, embedded_reference)
-
-    assert np.allclose(expected_value, value)  # Has correct value