Add __call__ and revert black

SeldonIO · jklaise · Aug 4, 2021 · Jul 30, 2021 · Jul 30, 2021 · Jul 30, 2021
commit 904f46c68876469aa606b8518e218f2243c16d1c
diff --git a/alibi/explainers/anchor_image.py b/alibi/explainers/anchor_image.py
@@ -18,22 +18,16 @@
 logger = logging.getLogger(__name__)
 
 DEFAULT_SEGMENTATION_KWARGS = {
-    "felzenszwalb": {},
-    "quickshift": {},
-    "slic": {"n_segments": 10, "compactness": 10, "sigma": 0.5},
+    'felzenszwalb': {},
+    'quickshift': {},
+    'slic': {'n_segments': 10, 'compactness': 10, 'sigma': .5}
 }
 
 
 class AnchorImage(Explainer):
-    def __init__(
-        self,
-        predictor: Callable,
-        image_shape: tuple,
-        segmentation_fn: Any = "slic",
-        segmentation_kwargs: dict = None,
-        images_background: np.ndarray = None,
-        seed: int = None,
-    ) -> None:
+    def __init__(self, predictor: Callable, image_shape: tuple, segmentation_fn: Any = 'slic',
+                 segmentation_kwargs: dict = None, images_background: np.ndarray = None,
+                 seed: int = None) -> None:
         """
         Initialize anchor image explainer.
 
@@ -62,54 +56,48 @@ def __init__(
                 segmentation_kwargs = DEFAULT_SEGMENTATION_KWARGS[segmentation_fn]  # type: ignore
             except KeyError:
                 logger.warning(
-                    "DEFAULT_SEGMENTATION_KWARGS did not contain any entry"
-                    "for segmentation method {}. No kwargs will be passed to"
-                    "the segmentation function!".format(segmentation_fn)
+                    'DEFAULT_SEGMENTATION_KWARGS did not contain any entry'
+                    'for segmentation method {}. No kwargs will be passed to'
+                    'the segmentation function!'.format(segmentation_fn)
                 )
                 segmentation_kwargs = {}
         elif callable(segmentation_fn) and segmentation_kwargs:
             logger.warning(
-                "Specified both a segmentation function to create superpixels and "
-                "keyword arguments for built segmentation functions. By default "
-                "the specified segmentation function will be used."
+                'Specified both a segmentation function to create superpixels and '
+                'keyword arguments for built segmentation functions. By default '
+                'the specified segmentation function will be used.'
             )
 
         # set the predictor
         self.image_shape = image_shape
         self.predictor = self._transform_predictor(predictor)
 
         # segmentation function is either a user-defined function or one of the values in
-        fn_options = {
-            "felzenszwalb": felzenszwalb,
-            "slic": slic,
-            "quickshift": quickshift,
-        }
+        fn_options = {'felzenszwalb': felzenszwalb, 'slic': slic, 'quickshift': quickshift}
         if callable(segmentation_fn):
             self.custom_segmentation = True
             self.segmentation_fn = segmentation_fn
         else:
             self.custom_segmentation = False
-            self.segmentation_fn = partial(
-                fn_options[segmentation_fn], **segmentation_kwargs
-            )
+            self.segmentation_fn = partial(fn_options[segmentation_fn], **segmentation_kwargs)
 
         self.images_background = images_background
         # a superpixel is perturbed with prob 1 - p_sample
         self.p_sample = 0.5  # type: float
 
         # update metadata
-        self.meta["params"].update(
+        self.meta['params'].update(
             custom_segmentation=self.custom_segmentation,
             segmentation_kwargs=segmentation_kwargs,
             p_sample=self.p_sample,
             seed=seed,
             image_shape=self.image_shape,
-            images_background=self.images_background,
+            images_background=self.images_background
         )
         if not self.custom_segmentation:
-            self.meta["params"].update(segmentation_fn=segmentation_fn)
+            self.meta['params'].update(segmentation_fn=segmentation_fn)
         else:
-            self.meta["params"].update(segmentation_fn="custom")
+            self.meta['params'].update(segmentation_fn='custom')
 
     def generate_superpixels(self, image: np.ndarray) -> np.ndarray:
         """
@@ -221,7 +209,7 @@ def explain(
         """
         # get params for storage in meta
         params = locals()
-        remove = ["image", "self"]
+        remove = ['image', 'self']
         for key in remove:
             params.pop(key)
 
@@ -237,11 +225,10 @@ def explain(
 
         # get anchors and add metadata
         mab = AnchorBaseBeam(
-            samplers=[sampler.sample],
+            samplers=[sampler],
             sample_cache_size=binary_cache_size,
             cache_margin=cache_margin,
-            **kwargs,
-        )
+            **kwargs)
         result = mab.anchor_beam(
             desired_confidence=threshold,
             delta=delta,
@@ -285,13 +272,13 @@ def build_explanation(
             Parameters passed to `explain`
         """
 
-        result["instance"] = image
-        result["instances"] = np.expand_dims(image, 0)
-        result["prediction"] = np.array([predicted_label])
+        result['instance'] = image
+        result['instances'] = np.expand_dims(image, 0)
+        result['prediction'] = np.array([predicted_label])
 
         # overlay image with anchor mask
-        anchor = self.overlay_mask(image, sampler.segments, result["feature"])
-        exp = AnchorExplanation("image", result)
+        anchor = self.overlay_mask(image, sampler.segments, result['feature'])
+        exp = AnchorExplanation('image', result)
 
         # output explanation dictionary
         data = copy.deepcopy(DEFAULT_DATA_ANCHOR_IMG)
@@ -300,23 +287,18 @@ def build_explanation(
             segments=sampler.segments,
             precision=exp.precision(),
             coverage=exp.coverage(),
-            raw=exp.exp_map,
+            raw=exp.exp_map
         )
 
         # create explanation object
         explanation = Explanation(meta=copy.deepcopy(self.meta), data=data)
 
         # params passed to explain
-        explanation.meta["params"].update(params)
+        explanation.meta['params'].update(params)
         return explanation
 
-    def overlay_mask(
-        self,
-        image: np.ndarray,
-        segments: np.ndarray,
-        mask_features: list,
-        scale: tuple = (0, 255),
-    ) -> np.ndarray:
+    def overlay_mask(self, image: np.ndarray, segments: np.ndarray, mask_features: list,
+                     scale: tuple = (0, 255)) -> np.ndarray:
         """
         Overlay image with mask described by the mask features.
 

diff --git a/alibi/explainers/anchor_image_sampler.py b/alibi/explainers/anchor_image_sampler.py
@@ -48,7 +48,7 @@ def __init__(
         self.segment_labels = list(np.unique(self.segments))
         self.instance_label = self.predictor(image[np.newaxis, ...])[0]
 
-    def sample(
+    def __call__(
         self, anchor: Tuple[int, tuple], num_samples: int, compute_labels: bool = True
     ) -> List[Union[np.ndarray, float, int]]:
         """

diff --git a/alibi/explainers/tests/test_anchor_image_sampler.py b/alibi/explainers/tests/test_anchor_image_sampler.py
@@ -54,7 +54,7 @@ def test_sampler(models, mnist_data):
     assert superpixels_mask.sum(axis=1).any() <= segmentation_kwargs["n_segments"]
     assert superpixels_mask.any() <= 1
 
-    cov_true, cov_false, labels, data, coverage, _ = sampler.sample(
+    cov_true, cov_false, labels, data, coverage, _ = sampler(
         (0, ()), num_samples
     )
     assert data.shape[0] == labels.shape[0]