Tidy up and fix issues

spacy/cli/converters/conllu2json.py

@@ -235,7 +235,7 @@ def example_from_conllu_sentence(
             subtok_word = ""
             in_subtok = False
         id_ = int(id_) - 1
-        head = (int(head) - 1) if head != "0" else id_
+        head = (int(head) - 1) if head not in ("0", "_") else id_
         tag = pos if tag == "_" else tag
         morph = morph if morph != "_" else ""
         dep = "ROOT" if dep == "root" else dep

spacy/errors.py

@@ -541,8 +541,8 @@ class Errors(object):
     E997 = ("Tokenizer special cases are not allowed to modify the text. "
             "This would map '{chunk}' to '{orth}' given token attributes "
             "'{token_attrs}'.")
-    E998 = ("Can only create GoldParse's from Example's without a Doc, "
+    E998 = ("Can only create GoldParse objects from Example objects without a "
-            "if get_gold_parses() is called with a Vocab object.")
+            "Doc if get_gold_parses() is called with a Vocab object.")
     E999 = ("Encountered an unexpected format for the dictionary holding "
             "gold annotations: {gold_dict}")
 

spacy/gold.pyx

@@ -991,11 +991,6 @@ cdef class GoldParse:
         self.cats = {} if cats is None else dict(cats)
         self.links = {} if links is None else dict(links)
 
-        # orig_annot is used as an iterator in `nlp.evalate` even if self.length == 0,
-        # so set a empty list to avoid error.
-        # if self.lenght > 0, this is modified latter.
-        self.orig_annot = []
-
         # avoid allocating memory if the doc does not contain any tokens
         if self.length > 0:
             if not words:

spacy/lang/sk/examples.py

@@ -1,7 +1,3 @@
-# coding: utf8
-from __future__ import unicode_literals
-
-
 """
 Example sentences to test spaCy and its language models.
 

spacy/lang/sk/lex_attrs.py

@@ -1,6 +1,3 @@
-# coding: utf8
-from __future__ import unicode_literals
-
 from ...attrs import LIKE_NUM
 
 _num_words = [

spacy/syntax/nn_parser.pyx

@@ -77,7 +77,7 @@ cdef class Parser:
         tok2vec = Tok2Vec(width=token_vector_width,
                           embed_size=embed_size,
                           conv_depth=conv_depth,
-                          window_size=window_size,
+                          window_size=conv_window,
                           cnn_maxout_pieces=t2v_pieces,
                           subword_features=subword_features,
                           pretrained_vectors=pretrained_vectors,
@@ -105,7 +105,7 @@ cdef class Parser:
             'bilstm_depth': bilstm_depth,
             'self_attn_depth': self_attn_depth,
             'conv_depth': conv_depth,
-            'window_size': window_size,
+            'window_size': conv_window,
             'embed_size': embed_size,
             'cnn_maxout_pieces': t2v_pieces
         }

spacy/tests/regression/test_issue4849.py

@@ -1,6 +1,3 @@
-# coding: utf8
-from __future__ import unicode_literals
-
 from spacy.lang.en import English
 from spacy.pipeline import EntityRuler
 
@@ -9,11 +6,12 @@ def test_issue4849():
     nlp = English()
 
     ruler = EntityRuler(
-        nlp, patterns=[
+        nlp,
-            {"label": "PERSON", "pattern": 'joe biden', "id": 'joe-biden'},
+        patterns=[
-            {"label": "PERSON", "pattern": 'bernie sanders', "id": 'bernie-sanders'},
+            {"label": "PERSON", "pattern": "joe biden", "id": "joe-biden"},
+            {"label": "PERSON", "pattern": "bernie sanders", "id": "bernie-sanders"},
         ],
-        phrase_matcher_attr="LOWER"
+        phrase_matcher_attr="LOWER",
     )
 
     nlp.add_pipe(ruler)
@@ -27,10 +25,10 @@ def test_issue4849():
     count_ents = 0
     for doc in nlp.pipe([text], n_process=1):
         count_ents += len([ent for ent in doc.ents if ent.ent_id > 0])
-    assert(count_ents == 2)
+    assert count_ents == 2
 
     # USING 2 PROCESSES
     count_ents = 0
     for doc in nlp.pipe([text], n_process=2):
         count_ents += len([ent for ent in doc.ents if ent.ent_id > 0])
-    assert (count_ents == 2)
+    assert count_ents == 2

spacy/tests/regression/test_issue4924.py

@@ -1,16 +1,9 @@
-# coding: utf8
-from __future__ import unicode_literals
-
 import pytest
-
+from spacy.language import Language
-import spacy
 
 
-@pytest.fixture
+def test_evaluate():
-def nlp():
+    nlp = Language()
-    return spacy.blank("en")
-
-
-def test_evaluate(nlp):
     docs_golds = [("", {})]
-    nlp.evaluate(docs_golds)
+    with pytest.raises(ValueError):
+        nlp.evaluate(docs_golds)