Restructure Sentencizer to follow Pipe API (#4721)

* Restructure Sentencizer to follow Pipe API

Restructure Sentencizer to follow Pipe API so that it can be scored with
`nlp.evaluate()`.

* Add Sentencizer pipe() test

spacy/pipeline/pipes.pyx

@@ -1464,21 +1464,59 @@ class Sentencizer(object):
 
         DOCS: https://spacy.io/api/sentencizer#call
         """
-        start = 0
-        seen_period = False
-        for i, token in enumerate(doc):
-            is_in_punct_chars = token.text in self.punct_chars
-            token.is_sent_start = i == 0
-            if seen_period and not token.is_punct and not is_in_punct_chars:
-                doc[start].is_sent_start = True
-                start = token.i
-                seen_period = False
-            elif is_in_punct_chars:
-                seen_period = True
-        if start < len(doc):
-            doc[start].is_sent_start = True
+        tags = self.predict([doc])
+        self.set_annotations([doc], tags)
         return doc
 
+    def pipe(self, stream, batch_size=128, n_threads=-1):
+        for docs in util.minibatch(stream, size=batch_size):
+            docs = list(docs)
+            tag_ids = self.predict(docs)
+            self.set_annotations(docs, tag_ids)
+            yield from docs
+
+    def predict(self, docs):
+        """Apply the pipeline's model to a batch of docs, without
+        modifying them.
+        """
+        if not any(len(doc) for doc in docs):
+            # Handle cases where there are no tokens in any docs.
+            guesses = [[] for doc in docs]
+            return guesses
+        guesses = []
+        for doc in docs:
+            start = 0
+            seen_period = False
+            doc_guesses = [False] * len(doc)
+            doc_guesses[0] = True
+            for i, token in enumerate(doc):
+                is_in_punct_chars = token.text in self.punct_chars
+                if seen_period and not token.is_punct and not is_in_punct_chars:
+                    doc_guesses[start] = True
+                    start = token.i
+                    seen_period = False
+                elif is_in_punct_chars:
+                    seen_period = True
+            if start < len(doc):
+                doc_guesses[start] = True
+            guesses.append(doc_guesses)
+        return guesses
+
+    def set_annotations(self, docs, batch_tag_ids, tensors=None):
+        if isinstance(docs, Doc):
+            docs = [docs]
+        cdef Doc doc
+        cdef int idx = 0
+        for i, doc in enumerate(docs):
+            doc_tag_ids = batch_tag_ids[i]
+            for j, tag_id in enumerate(doc_tag_ids):
+                # Don't clobber existing sentence boundaries
+                if doc.c[j].sent_start == 0:
+                    if tag_id:
+                        doc.c[j].sent_start = 1
+                    else:
+                        doc.c[j].sent_start = -1
+
     def to_bytes(self, **kwargs):
         """Serialize the sentencizer to a bytestring.
 

spacy/tests/pipeline/test_sentencizer.py

@@ -5,6 +5,7 @@ import pytest
 import spacy
 from spacy.pipeline import Sentencizer
 from spacy.tokens import Doc
+from spacy.lang.en import English
 
 
 def test_sentencizer(en_vocab):
@@ -17,6 +18,17 @@ def test_sentencizer(en_vocab):
     assert len(list(doc.sents)) == 2
 
 
+def test_sentencizer_pipe():
+    texts = ["Hello! This is a test.", "Hi! This is a test."]
+    nlp = English()
+    nlp.add_pipe(nlp.create_pipe("sentencizer"))
+    for doc in nlp.pipe(texts):
+        assert doc.is_sentenced
+        sent_starts = [t.is_sent_start for t in doc]
+        assert sent_starts == [True, False, True, False, False, False, False]
+        assert len(list(doc.sents)) == 2
+
+
 @pytest.mark.parametrize(
     "words,sent_starts,n_sents",
     [