[FIX] pass flake8

abl/data/structures/list_data.py

@@ -16,6 +16,7 @@ LongTypeTensor = Union[torch.LongTensor, torch.cuda.LongTensor]
 
 IndexType = Union[str, slice, int, list, LongTypeTensor, BoolTypeTensor, np.ndarray]
 
+
 class ListData(BaseDataElement):
     """
     Abstract Data Interface used throughout the ABL-Package.

abl/learning/basic_nn.py

@@ -309,7 +309,7 @@ class BasicNN:
                 batch_size=self.batch_size,
                 num_workers=self.num_workers,
                 collate_fn=self.collate_fn,
-                pin_memory=torch.cuda.is_available()
+                pin_memory=torch.cuda.is_available(),
             )
         return self._predict(data_loader).argmax(axis=1).cpu().numpy()
 
@@ -351,7 +351,7 @@ class BasicNN:
                 batch_size=self.batch_size,
                 num_workers=self.num_workers,
                 collate_fn=self.collate_fn,
-                pin_memory=torch.cuda.is_available()
+                pin_memory=torch.cuda.is_available(),
             )
         return self._predict(data_loader).softmax(axis=1).cpu().numpy()
 
@@ -484,7 +484,7 @@ class BasicNN:
             shuffle=shuffle,
             num_workers=self.num_workers,
             collate_fn=self.collate_fn,
-            pin_memory=torch.cuda.is_available()
+            pin_memory=torch.cuda.is_available(),
         )
         return data_loader
 

abl/reasoning/kb.py

@@ -128,10 +128,10 @@ class KBBase(ABC):
         Returns
         -------
         Tuple[List[List[Any]], List[Any]]
-            A tuple of two elements. The first element is a list of candidate revisions, i.e. revised
-            pseudo-labels of the example. that are compatible with the knowledge base. The second
-            element is a list of reasoning results corresponding to each candidate, i.e., the
-            outcome of the ``logic_forward`` function.
+            A tuple of two elements. The first element is a list of candidate revisions,
+            i.e. revised pseudo-labels of the example. that are compatible with the knowledge
+            base. The second element is a list of reasoning results corresponding to each
+            candidate, i.e., the outcome of the ``logic_forward`` function.
         """
         return self._abduce_by_search(pseudo_label, y, x, max_revision_num, require_more_revision)
 
@@ -179,10 +179,10 @@ class KBBase(ABC):
         Returns
         -------
         Tuple[List[List[Any]], List[Any]]
-            A tuple of two elements. The first element is a list of candidate revisions, i.e. revised
-            pseudo-labels of the example that are compatible with the knowledge base. The second
-            element is a list of reasoning results corresponding to each candidate, i.e., the
-            outcome of the ``logic_forward`` function.
+            A tuple of two elements. The first element is a list of candidate revisions,
+            i.e. revised pseudo-labels of the example. that are compatible with the knowledge
+            base. The second element is a list of reasoning results corresponding to each
+            candidate, i.e., the outcome of the ``logic_forward`` function.
         """
         candidates, reasoning_results = [], []
         abduce_c = product(self.pseudo_label_list, repeat=len(revision_idx))
@@ -249,10 +249,10 @@ class KBBase(ABC):
         Returns
         -------
         Tuple[List[List[Any]], List[Any]]
-            A tuple of two elements. The first element is a list of candidate revisions, i.e. revised
-            pseudo-labels of the example that are compatible with the knowledge base. The second
-            element is a list of reasoning results corresponding to each candidate, i.e., the
-            outcome of the ``logic_forward`` function.
+            A tuple of two elements. The first element is a list of candidate revisions,
+            i.e. revised pseudo-labels of the example. that are compatible with the knowledge
+            base. The second element is a list of reasoning results corresponding to each
+            candidate, i.e., the outcome of the ``logic_forward`` function.
         """
         candidates, reasoning_results = [], []
         for revision_num in range(len(pseudo_label) + 1):
@@ -389,10 +389,10 @@ class GroundKB(KBBase):
         Returns
         -------
         Tuple[List[List[Any]], List[Any]]
-            A tuple of two elements. The first element is a list of candidate revisions, i.e. revised
-            pseudo-labels of the example that are compatible with the knowledge base. The second
-            element is a list of reasoning results corresponding to each candidate, i.e., the
-            outcome of the ``logic_forward`` function.
+            A tuple of two elements. The first element is a list of candidate revisions,
+            i.e. revised pseudo-labels of the example. that are compatible with the knowledge
+            base. The second element is a list of reasoning results corresponding to each
+            candidate, i.e., the outcome of the ``logic_forward`` function.
         """
         if self.GKB == {} or len(pseudo_label) not in self.GKB_len_list:
             return [], []
@@ -592,10 +592,10 @@ class PrologKB(KBBase):
         Returns
         -------
         Tuple[List[List[Any]], List[Any]]
-            A tuple of two elements. The first element is a list of candidate revisions, i.e. revised
-            pseudo-labels of the example that are compatible with the knowledge base. The second
-            element is a list of reasoning results corresponding to each candidate, i.e., the
-            outcome of the ``logic_forward`` function.
+            A tuple of two elements. The first element is a list of candidate revisions,
+            i.e. revised pseudo-labels of the example. that are compatible with the knowledge
+            base. The second element is a list of reasoning results corresponding to each
+            candidate, i.e., the outcome of the ``logic_forward`` function.
         """
         candidates, reasoning_results = [], []
         query_string = self.get_query_string(pseudo_label, y, x, revision_idx)

abl/utils/utils.py

@@ -109,6 +109,7 @@ def confidence_dist(pred_prob: np.ndarray, candidates_idxs: List[List[Any]]) ->
     cols = np.arange(len(candidates_idxs[0]))[None, :]
     return 1 - np.prod(pred_prob[cols, candidates_idxs], axis=1)
 
+
 def avg_confidence_dist(pred_prob: np.ndarray, candidates_idxs: List[List[Any]]) -> np.ndarray:
     """
     Compute the average confidence distance between prediction probabilities and candidates,
@@ -130,6 +131,7 @@ def avg_confidence_dist(pred_prob: np.ndarray, candidates_idxs: List[List[Any]])
     cols = np.arange(len(candidates_idxs[0]))[None, :]
     return 1 - np.average(pred_prob[cols, candidates_idxs], axis=1)
 
+
 def to_hashable(x: Union[List[Any], Any]) -> Union[Tuple[Any, ...], Any]:
     """
     Convert a nested list to a nested tuple so it is hashable.