Tidy up regression tests

spacy/tests/regression/test_issue351.py

@@ -1,4 +1,6 @@
+# coding: utf-8
 from __future__ import unicode_literals
+
 from ...en import English
 
 import pytest

spacy/tests/regression/test_issue360.py

@@ -1,4 +1,6 @@
+# coding: utf-8
 from __future__ import unicode_literals
+
 from ...en import English
 
 import pytest
@@ -10,5 +12,5 @@ def en_tokenizer():
 
 
 def test_big_ellipsis(en_tokenizer):
-    tokens = en_tokenizer(u'$45...............Asking')
+    tokens = en_tokenizer('$45...............Asking')
     assert len(tokens) > 2

spacy/tests/regression/test_issue429.py

@@ -1,9 +1,11 @@
+# coding: utf-8
 from __future__ import unicode_literals
-import pytest
 
 import spacy
 from spacy.attrs import ORTH
 
+import pytest
+
 
 @pytest.mark.models
 def test_issue429():

spacy/tests/regression/test_issue587.py

@@ -1,14 +1,18 @@
+# coding: utf-8
+from __future__ import unicode_literals
+
 import spacy
 import spacy.matcher
 from spacy.attrs import IS_PUNCT, ORTH
 
 import pytest
 
+
 @pytest.mark.models
 def test_matcher_segfault():
     nlp = spacy.load('en', parser=False, entity=False)
     matcher = spacy.matcher.Matcher(nlp.vocab)
-    content = u'''a b; c'''
+    content = '''a b; c'''
     matcher.add(entity_key='1', label='TEST', attrs={}, specs=[[{ORTH: 'a'}, {ORTH: 'b'}]])
     matcher(nlp(content))
     matcher.add(entity_key='2', label='TEST', attrs={}, specs=[[{ORTH: 'a'}, {ORTH: 'b'}, {IS_PUNCT: True}, {ORTH: 'c'}]])

spacy/tests/regression/test_issue588.py

@@ -1,8 +1,12 @@
-import pytest
+# coding: utf-8
+from __future__ import unicode_literals
+
 from ...vocab import Vocab
 from ...tokens import Doc
 from ...matcher import Matcher
 
+import pytest
+
 
 def test_issue588():
     matcher = Matcher(Vocab())

spacy/tests/regression/test_issue589.py

@@ -1,10 +1,13 @@
-import pytest
+# coding: utf-8
+from __future__ import unicode_literals
 
 from ...vocab import Vocab
 from ...tokens import Doc
 
+import pytest
+
 
 def test_issue589():
     vocab = Vocab()
     vocab.strings.set_frozen(True)
-    doc = Doc(vocab, words=[u'whata'])
+    doc = Doc(vocab, words=['whata'])

spacy/tests/regression/test_issue590.py

@@ -1,9 +1,12 @@
+# coding: utf-8
 from __future__ import unicode_literals
+
 from ...attrs import *
 from ...matcher import Matcher
 from ...tokens import Doc
 from ...en import English
 
+
 def test_overlapping_matches():
     vocab = English.Defaults.create_vocab()
     doc = Doc(vocab, words=['n', '=', '1', ';', 'a', ':', '5', '%'])

spacy/tests/regression/test_issue595.py

@@ -1,11 +1,13 @@
+# coding: utf-8
 from __future__ import unicode_literals
-import pytest
 
 from ...symbols import POS, VERB, VerbForm_inf
 from ...tokens import Doc
 from ...vocab import Vocab
 from ...lemmatizer import Lemmatizer
 
+import pytest
+
 
 @pytest.fixture
 def index():
@@ -37,6 +39,6 @@ def vocab(lemmatizer, tag_map):
 def test_not_lemmatize_base_forms(vocab):
     doc = Doc(vocab, words=["Do", "n't", "feed", "the", "dog"])
     feed = doc[2]
-    feed.tag_ = u'VB'
+    feed.tag_ = 'VB'
-    assert feed.text == u'feed'
+    assert feed.text == 'feed'
-    assert feed.lemma_ == u'feed'
+    assert feed.lemma_ == 'feed'

spacy/tests/regression/test_issue599.py

@@ -1,6 +1,10 @@
+# coding: utf-8
+from __future__ import unicode_literals
+
 from ...tokens import Doc
 from ...vocab import Vocab
 
+
 def test_issue599():
     doc = Doc(Vocab())
     doc.is_tagged = True

spacy/tests/regression/test_issue600.py

@@ -1,4 +1,6 @@
+# coding: utf-8
 from __future__ import unicode_literals
+
 from ...tokens import Doc
 from ...vocab import Vocab
 from ...attrs import POS
@@ -6,4 +8,4 @@ from ...attrs import POS
 
 def test_issue600():
     doc = Doc(Vocab(tag_map={'NN': {'pos': 'NOUN'}}), words=['hello'])
-    doc[0].tag_ = u'NN'
+    doc[0].tag_ = 'NN'

spacy/tests/regression/test_issue605.py

@@ -1,3 +1,6 @@
+# coding: utf-8
+from __future__ import unicode_literals
+
 from ...attrs import LOWER, ORTH
 from ...tokens import Doc
 from ...vocab import Vocab
@@ -9,7 +12,7 @@ def return_false(doc, ent_id, label, start, end):
 
 
 def test_matcher_accept():
-    doc = Doc(Vocab(), words=[u'The', u'golf', u'club', u'is', u'broken'])
+    doc = Doc(Vocab(), words=['The', 'golf', 'club', 'is', 'broken'])
 
     golf_pattern =     [
         { ORTH: "golf"},
@@ -17,8 +20,8 @@ def test_matcher_accept():
     ]
     matcher = Matcher(doc.vocab)
 
-    matcher.add_entity(u'Sport_Equipment', acceptor=return_false)
+    matcher.add_entity('Sport_Equipment', acceptor=return_false)
-    matcher.add_pattern(u"Sport_Equipment", golf_pattern)
+    matcher.add_pattern("Sport_Equipment", golf_pattern)
     match = matcher(doc)
 
     assert match == []

spacy/tests/regression/test_issue615.py

@@ -1,4 +1,6 @@
+# coding: utf-8
 from __future__ import unicode_literals
+
 import spacy
 from spacy.attrs import ORTH
 
@@ -17,7 +19,7 @@ def merge_phrases(matcher, doc, i, matches):
 
 def test_entity_ID_assignment():
     nlp = spacy.en.English()
-    text = u"""The golf club is broken"""
+    text = """The golf club is broken"""
     doc = nlp(text)
 
     golf_pattern =     [

spacy/tests/regression/test_issue617.py

@@ -1,3 +1,6 @@
+# coding: utf-8
+from __future__ import unicode_literals
+
 from ...vocab import Vocab