Fix formatting

spacy/pipeline.pyx

@@ -48,7 +48,7 @@ from .parts_of_speech import X
 
 
 class SentenceSegmenter(object):
-    '''A simple spaCy hook, to allow custom sentence boundary detection logic
+    """A simple spaCy hook, to allow custom sentence boundary detection logic
     (that doesn't require the dependency parse).
 
     To change the sentence boundary detection strategy, pass a generator
@@ -57,7 +57,7 @@ class SentenceSegmenter(object):
 
     Sentence detection strategies should be generators that take `Doc` objects
     and yield `Span` objects for each sentence.
-    '''
+    """
     name = 'sbd'
 
     def __init__(self, vocab, strategy=None):
@@ -89,30 +89,30 @@ class BaseThincComponent(object):
 
     @classmethod
     def Model(cls, *shape, **kwargs):
-        '''Initialize a model for the pipe.'''
+        """Initialize a model for the pipe."""
         raise NotImplementedError
 
     def __init__(self, vocab, model=True, **cfg):
-        '''Create a new pipe instance.'''
+        """Create a new pipe instance."""
         raise NotImplementedError
 
     def __call__(self, doc):
-        '''Apply the pipe to one document. The document is
+        """Apply the pipe to one document. The document is
         modified in-place, and returned.
-        
+
         Both __call__ and pipe should delegate to the `predict()`
         and `set_annotations()` methods.
-        '''
+        """
         scores = self.predict([doc])
         self.set_annotations([doc], scores)
         return doc
 
     def pipe(self, stream, batch_size=128, n_threads=-1):
-        '''Apply the pipe to a stream of documents.
+        """Apply the pipe to a stream of documents.
 
         Both __call__ and pipe should delegate to the `predict()`
         and `set_annotations()` methods.
-        '''
+        """
         for docs in cytoolz.partition_all(batch_size, stream):
             docs = list(docs)
             scores = self.predict(docs)
@@ -120,43 +120,43 @@ class BaseThincComponent(object):
             yield from docs
 
     def predict(self, docs):
-        '''Apply the pipeline's model to a batch of docs, without
+        """Apply the pipeline's model to a batch of docs, without
         modifying them.
-        '''
+        """
         raise NotImplementedError
 
     def set_annotations(self, docs, scores):
-        '''Modify a batch of documents, using pre-computed scores.'''
+        """Modify a batch of documents, using pre-computed scores."""
         raise NotImplementedError
 
     def update(self, docs, golds, drop=0., sgd=None, losses=None):
-        '''Learn from a batch of documents and gold-standard information,
+        """Learn from a batch of documents and gold-standard information,
         updating the pipe's model.
 
         Delegates to predict() and get_loss().
-        '''
+        """
         raise NotImplementedError
 
     def get_loss(self, docs, golds, scores):
-        '''Find the loss and gradient of loss for the batch of
-        documents and their predicted scores.'''
+        """Find the loss and gradient of loss for the batch of
+        documents and their predicted scores."""
         raise NotImplementedError
 
     def begin_training(self, gold_tuples=tuple(), pipeline=None):
-        '''Initialize the pipe for training, using data exampes if available.
-        If no model has been initialized yet, the model is added.'''
+        """Initialize the pipe for training, using data exampes if available.
+        If no model has been initialized yet, the model is added."""
         if self.model is True:
             self.model = self.Model(**self.cfg)
         link_vectors_to_models(self.vocab)
 
     def use_params(self, params):
-        '''Modify the pipe's model, to use the given parameter values.
-        '''
+        """Modify the pipe's model, to use the given parameter values.
+        """
         with self.model.use_params(params):
             yield
 
     def to_bytes(self, **exclude):
-        '''Serialize the pipe to a bytestring.'''
+        """Serialize the pipe to a bytestring."""
         serialize = OrderedDict((
             ('cfg', lambda: json_dumps(self.cfg)),
             ('model', lambda: self.model.to_bytes()),
@@ -165,7 +165,7 @@ class BaseThincComponent(object):
         return util.to_bytes(serialize, exclude)
 
     def from_bytes(self, bytes_data, **exclude):
-        '''Load the pipe from a bytestring.'''
+        """Load the pipe from a bytestring."""
         def load_model(b):
             if self.model is True:
                 self.cfg['pretrained_dims'] = self.vocab.vectors_length
@@ -181,7 +181,7 @@ class BaseThincComponent(object):
         return self
 
     def to_disk(self, path, **exclude):
-        '''Serialize the pipe to disk.'''
+        """Serialize the pipe to disk."""
         serialize = OrderedDict((
             ('cfg', lambda p: p.open('w').write(json_dumps(self.cfg))),
             ('vocab', lambda p: self.vocab.to_disk(p)),
@@ -190,7 +190,7 @@ class BaseThincComponent(object):
         util.to_disk(path, serialize, exclude)
 
     def from_disk(self, path, **exclude):
-        '''Load the pipe from disk.'''
+        """Load the pipe from disk."""
         def load_model(p):
             if self.model is True:
                 self.cfg['pretrained_dims'] = self.vocab.vectors_length
@@ -596,7 +596,7 @@ class SimilarityHook(BaseThincComponent):
         return Siamese(Pooling(max_pool, mean_pool), CauchySimilarity(length))
 
     def __call__(self, doc):
-        '''Install similarity hook'''
+        """Install similarity hook"""
         doc.user_hooks['similarity'] = self.predict
         return doc