spaCy/spacy/tests/doc/test_creation.py

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💫 Refactor test suite (#2568) ## Description Related issues: #2379 (should be fixed by separating model tests) * **total execution time down from > 300 seconds to under 60 seconds** 🎉 * removed all model-specific tests that could only really be run manually anyway – those will now live in a separate test suite in the [`spacy-models`](https://github.com/explosion/spacy-models) repository and are already integrated into our new model training infrastructure * changed all relative imports to absolute imports to prepare for moving the test suite from `/spacy/tests` to `/tests` (it'll now always test against the installed version) * merged old regression tests into collections, e.g. `test_issue1001-1500.py` (about 90% of the regression tests are very short anyways) * tidied up and rewrote existing tests wherever possible ### Todo - [ ] move tests to `/tests` and adjust CI commands accordingly - [x] move model test suite from internal repo to `spacy-models` - [x] ~~investigate why `pipeline/test_textcat.py` is flakey~~ - [x] review old regression tests (leftover files) and see if they can be merged, simplified or deleted - [ ] update documentation on how to run tests ### Types of change enhancement, tests ## Checklist <!--- Before you submit the PR, go over this checklist and make sure you can tick off all the boxes. [] -> [x] --> - [x] I have submitted the spaCy Contributor Agreement. - [x] I ran the tests, and all new and existing tests passed. - [ ] My changes don't require a change to the documentation, or if they do, I've added all required information.
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import pytest
from spacy.vocab import Vocab
from spacy.tokens import Doc
from spacy.lemmatizer import Lemmatizer
from spacy.lookups import Lookups
from spacy import util
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@pytest.fixture
def lemmatizer():
lookups = Lookups()
lookups.add_table("lemma_lookup", {"dogs": "dog", "boxen": "box", "mice": "mouse"})
return Lemmatizer(lookups)
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@pytest.fixture
def vocab(lemmatizer):
return Vocab(lemmatizer=lemmatizer)
def test_empty_doc(vocab):
doc = Doc(vocab)
assert len(doc) == 0
def test_single_word(vocab):
doc = Doc(vocab, words=["a"])
assert doc.text == "a "
doc = Doc(vocab, words=["a"], spaces=[False])
assert doc.text == "a"
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def test_lookup_lemmatization(vocab):
doc = Doc(vocab, words=["dogs", "dogses"])
assert doc[0].text == "dogs"
assert doc[0].lemma_ == "dog"
assert doc[1].text == "dogses"
assert doc[1].lemma_ == "dogses"
def test_create_from_words_and_text(vocab):
# no whitespace in words
words = ["'", "dogs", "'", "run"]
text = " 'dogs'\n\nrun "
(words, spaces) = util.get_words_and_spaces(words, text)
doc = Doc(vocab, words=words, spaces=spaces)
assert [t.text for t in doc] == [" ", "'", "dogs", "'", "\n\n", "run", " "]
assert [t.whitespace_ for t in doc] == ["", "", "", "", "", " ", ""]
assert doc.text == text
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assert [t.text for t in doc if not t.text.isspace()] == [
word for word in words if not word.isspace()
]
# partial whitespace in words
words = [" ", "'", "dogs", "'", "\n\n", "run", " "]
text = " 'dogs'\n\nrun "
(words, spaces) = util.get_words_and_spaces(words, text)
doc = Doc(vocab, words=words, spaces=spaces)
assert [t.text for t in doc] == [" ", "'", "dogs", "'", "\n\n", "run", " "]
assert [t.whitespace_ for t in doc] == ["", "", "", "", "", " ", ""]
assert doc.text == text
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assert [t.text for t in doc if not t.text.isspace()] == [
word for word in words if not word.isspace()
]
# non-standard whitespace tokens
words = [" ", " ", "'", "dogs", "'", "\n\n", "run"]
text = " 'dogs'\n\nrun "
(words, spaces) = util.get_words_and_spaces(words, text)
doc = Doc(vocab, words=words, spaces=spaces)
assert [t.text for t in doc] == [" ", "'", "dogs", "'", "\n\n", "run", " "]
assert [t.whitespace_ for t in doc] == ["", "", "", "", "", " ", ""]
assert doc.text == text
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assert [t.text for t in doc if not t.text.isspace()] == [
word for word in words if not word.isspace()
]
# mismatch between words and text
with pytest.raises(ValueError):
words = [" ", " ", "'", "dogs", "'", "\n\n", "run"]
text = " 'dogs'\n\nrun "
(words, spaces) = util.get_words_and_spaces(words + ["away"], text)