Formatting

spacy/pipeline/coref.py

@@ -60,6 +60,7 @@ DEFAULT_MODEL = Config().from_str(default_config)["model"]
 
 DEFAULT_CLUSTERS_PREFIX = "coref_clusters"
 
+
 @Language.factory(
     "coref",
     assigns=["doc.spans"],
@@ -133,7 +134,7 @@ class CoreferenceResolver(TrainablePipe):
         # each item in scores includes scores and a mapping from scores to mentions
         ant_idxs = idxs
 
-        #TODO batching
+        # TODO batching
         xp = self.model.ops.xp
 
         starts = xp.arange(0, len(docs[0]))
@@ -242,7 +243,7 @@ class CoreferenceResolver(TrainablePipe):
         if self._rehearsal_model is None:
             return losses
         validate_examples(examples, "CoreferenceResolver.rehearse")
-        #TODO test this whole function
+        # TODO test this whole function
         docs = [eg.predicted for eg in examples]
         if not any(len(doc) for doc in docs):
             # Handle cases where there are no tokens in any docs.
@@ -256,7 +257,7 @@ class CoreferenceResolver(TrainablePipe):
         if sgd is not None:
             self.finish_update(sgd)
         if losses is not None:
-            losses[self.name] += (gradient ** 2).sum()
+            losses[self.name] += (gradient**2).sum()
         return losses
 
     def add_label(self, label: str) -> int:
@@ -290,13 +291,13 @@ class CoreferenceResolver(TrainablePipe):
         offset = 0
         gradients = []
         total_loss = 0
-        #TODO change this
+        # TODO change this
         # 1. do not handle batching (add it back later)
         # 2. don't do index conversion (no mentions, just word indices)
         # 3. convert words to spans (if necessary) in gold and predictions
-   
+
         # massage score matrix to be shaped correctly
-        score_matrix = [ (score_matrix, None) ]
+        score_matrix = [(score_matrix, None)]
         for example, (cscores, cidx) in zip(examples, score_matrix):
 
             ll = cscores.shape[0]
@@ -306,7 +307,7 @@ class CoreferenceResolver(TrainablePipe):
             span_idxs = create_head_span_idxs(ops, len(example.predicted))
             gscores = create_gold_scores(span_idxs, clusters)
             gscores = ops.asarray2f(gscores)
-            #top_gscores = xp.take_along_axis(gscores, cidx, axis=1)
+            # top_gscores = xp.take_along_axis(gscores, cidx, axis=1)
             top_gscores = xp.take_along_axis(gscores, mention_idx, axis=1)
             # now add the placeholder
             gold_placeholder = ~top_gscores.any(axis=1).T
@@ -322,7 +323,7 @@ class CoreferenceResolver(TrainablePipe):
             log_norm = ops.softmax(cscores, axis=1)
             grad = log_norm - log_marg
             gradients.append((grad, cidx))
-            total_loss += float((grad ** 2).sum())
+            total_loss += float((grad**2).sum())
 
             offset = hi
 
@@ -389,6 +390,7 @@ class CoreferenceResolver(TrainablePipe):
             out[fname] = mean([ss[fname] for ss in scores])
         return out
 
+
 class SpanPredictor(TrainablePipe):
     """Pipeline component to resolve one-token spans to full spans.
 
@@ -419,21 +421,21 @@ class SpanPredictor(TrainablePipe):
         ...
 
     def update(
-        self, 
-        examples: Iterable[Example], 
-        *, 
-        drop: float = 0.0, 
-        sgd: Optional[Optimizer] = None, 
+        self,
+        examples: Iterable[Example],
+        *,
+        drop: float = 0.0,
+        sgd: Optional[Optimizer] = None,
         losses: Optional[Dict[str, float]] = None,
     ) -> Dict[str, float]:
         ...
 
     def rehearse(
-        self, 
-        examples: Iterable[Example], 
-        *, 
-        drop: float = 0.0, 
-        sgd: Optional[Optimizer] = None, 
+        self,
+        examples: Iterable[Example],
+        *,
+        drop: float = 0.0,
+        sgd: Optional[Optimizer] = None,
         losses: Optional[Dict[str, float]] = None,
     ) -> Dict[str, float]:
         ...
@@ -451,7 +453,7 @@ class SpanPredictor(TrainablePipe):
     def get_loss(
         self,
         examples: Iterable[Example],
-        #TODO add necessary args
+        # TODO add necessary args
     ):
         ...
 
@@ -475,4 +477,3 @@ class SpanPredictor(TrainablePipe):
     def score(self, examples, **kwargs):
         # TODO this will overlap significantly with coref, maybe factor into function
         ...
-