mirror of
https://github.com/explosion/spaCy.git
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b6e991440c
* Auto-format tests with black * Add flake8 config * Tidy up and remove unused imports * Fix redefinitions of test functions * Replace orths_and_spaces with words and spaces * Fix compatibility with pytest 4.0 * xfail test for now Test was previously overwritten by following test due to naming conflict, so failure wasn't reported * Unfail passing test * Only use fixture via arguments Fixes pytest 4.0 compatibility
144 lines
4.2 KiB
Python
144 lines
4.2 KiB
Python
# coding: utf-8
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from __future__ import unicode_literals
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import pytest
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import re
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from spacy.tokens import Doc
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from spacy.vocab import Vocab
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from spacy.lang.en import English
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from spacy.lang.lex_attrs import LEX_ATTRS
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from spacy.matcher import Matcher
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from spacy.tokenizer import Tokenizer
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from spacy.lemmatizer import Lemmatizer
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from spacy.symbols import ORTH, LEMMA, POS, VERB, VerbForm_part
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def test_issue1242():
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nlp = English()
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doc = nlp("")
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assert len(doc) == 0
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docs = list(nlp.pipe(["", "hello"]))
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assert len(docs[0]) == 0
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assert len(docs[1]) == 1
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def test_issue1250():
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"""Test cached special cases."""
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special_case = [{ORTH: "reimbur", LEMMA: "reimburse", POS: "VERB"}]
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nlp = English()
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nlp.tokenizer.add_special_case("reimbur", special_case)
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lemmas = [w.lemma_ for w in nlp("reimbur, reimbur...")]
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assert lemmas == ["reimburse", ",", "reimburse", "..."]
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lemmas = [w.lemma_ for w in nlp("reimbur, reimbur...")]
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assert lemmas == ["reimburse", ",", "reimburse", "..."]
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def test_issue1257():
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"""Test that tokens compare correctly."""
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doc1 = Doc(Vocab(), words=["a", "b", "c"])
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doc2 = Doc(Vocab(), words=["a", "c", "e"])
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assert doc1[0] != doc2[0]
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assert not doc1[0] == doc2[0]
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def test_issue1375():
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"""Test that token.nbor() raises IndexError for out-of-bounds access."""
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doc = Doc(Vocab(), words=["0", "1", "2"])
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with pytest.raises(IndexError):
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assert doc[0].nbor(-1)
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assert doc[1].nbor(-1).text == "0"
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with pytest.raises(IndexError):
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assert doc[2].nbor(1)
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assert doc[1].nbor(1).text == "2"
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def test_issue1387():
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tag_map = {"VBG": {POS: VERB, VerbForm_part: True}}
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index = {"verb": ("cope", "cop")}
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exc = {"verb": {"coping": ("cope",)}}
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rules = {"verb": [["ing", ""]]}
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lemmatizer = Lemmatizer(index, exc, rules)
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vocab = Vocab(lemmatizer=lemmatizer, tag_map=tag_map)
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doc = Doc(vocab, words=["coping"])
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doc[0].tag_ = "VBG"
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assert doc[0].text == "coping"
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assert doc[0].lemma_ == "cope"
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def test_issue1434():
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"""Test matches occur when optional element at end of short doc."""
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pattern = [{"ORTH": "Hello"}, {"IS_ALPHA": True, "OP": "?"}]
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vocab = Vocab(lex_attr_getters=LEX_ATTRS)
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hello_world = Doc(vocab, words=["Hello", "World"])
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hello = Doc(vocab, words=["Hello"])
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matcher = Matcher(vocab)
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matcher.add("MyMatcher", None, pattern)
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matches = matcher(hello_world)
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assert matches
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matches = matcher(hello)
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assert matches
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@pytest.mark.parametrize(
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"string,start,end",
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[
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("a", 0, 1),
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("a b", 0, 2),
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("a c", 0, 1),
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("a b c", 0, 2),
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("a b b c", 0, 3),
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("a b b", 0, 3),
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],
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)
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def test_issue1450(string, start, end):
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"""Test matcher works when patterns end with * operator."""
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pattern = [{"ORTH": "a"}, {"ORTH": "b", "OP": "*"}]
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matcher = Matcher(Vocab())
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matcher.add("TSTEND", None, pattern)
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doc = Doc(Vocab(), words=string.split())
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matches = matcher(doc)
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if start is None or end is None:
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assert matches == []
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assert matches[-1][1] == start
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assert matches[-1][2] == end
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def test_issue1488():
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prefix_re = re.compile(r"""[\[\("']""")
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suffix_re = re.compile(r"""[\]\)"']""")
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infix_re = re.compile(r"""[-~\.]""")
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simple_url_re = re.compile(r"""^https?://""")
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def my_tokenizer(nlp):
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return Tokenizer(
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nlp.vocab,
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{},
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prefix_search=prefix_re.search,
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suffix_search=suffix_re.search,
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infix_finditer=infix_re.finditer,
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token_match=simple_url_re.match,
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)
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nlp = English()
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nlp.tokenizer = my_tokenizer(nlp)
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doc = nlp("This is a test.")
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for token in doc:
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assert token.text
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def test_issue1494():
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infix_re = re.compile(r"""[^a-z]""")
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test_cases = [
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("token 123test", ["token", "1", "2", "3", "test"]),
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("token 1test", ["token", "1test"]),
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("hello...test", ["hello", ".", ".", ".", "test"]),
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]
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def new_tokenizer(nlp):
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return Tokenizer(nlp.vocab, {}, infix_finditer=infix_re.finditer)
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nlp = English()
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nlp.tokenizer = new_tokenizer(nlp)
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for text, expected in test_cases:
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assert [token.text for token in nlp(text)] == expected
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