spaCy/spacy/tests/lang/en/test_exceptions.py

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import pytest
def test_en_tokenizer_handles_basic_contraction(en_tokenizer):
text = "don't giggle"
tokens = en_tokenizer(text)
assert len(tokens) == 3
assert tokens[1].text == "n't"
text = "i said don't!"
tokens = en_tokenizer(text)
assert len(tokens) == 5
assert tokens[4].text == "!"
@pytest.mark.parametrize("text", ["`ain't", """"isn't""", "can't!"])
def test_en_tokenizer_handles_basic_contraction_punct(en_tokenizer, text):
tokens = en_tokenizer(text)
assert len(tokens) == 3
@pytest.mark.parametrize(
"text_poss,text", [("Robin's", "Robin"), ("Alexis's", "Alexis")]
)
def test_en_tokenizer_handles_poss_contraction(en_tokenizer, text_poss, text):
tokens = en_tokenizer(text_poss)
assert len(tokens) == 2
assert tokens[0].text == text
assert tokens[1].text == "'s"
@pytest.mark.parametrize("text", ["schools'", "Alexis'"])
def test_en_tokenizer_splits_trailing_apos(en_tokenizer, text):
tokens = en_tokenizer(text)
assert len(tokens) == 2
assert tokens[0].text == text.split("'")[0]
assert tokens[1].text == "'"
@pytest.mark.parametrize("text", ["'em", "nothin'", "ol'"])
💫 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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def test_en_tokenizer_doesnt_split_apos_exc(en_tokenizer, text):
tokens = en_tokenizer(text)
assert len(tokens) == 1
assert tokens[0].text == text
@pytest.mark.parametrize("text", ["we'll", "You'll", "there'll", "this'll", "those'll"])
def test_en_tokenizer_handles_ll_contraction(en_tokenizer, text):
tokens = en_tokenizer(text)
assert len(tokens) == 2
assert tokens[0].text == text.split("'")[0]
assert tokens[1].text == "'ll"
@pytest.mark.parametrize(
"text_lower,text_title", [("can't", "Can't"), ("ain't", "Ain't")]
)
def test_en_tokenizer_handles_capitalization(en_tokenizer, text_lower, text_title):
tokens_lower = en_tokenizer(text_lower)
tokens_title = en_tokenizer(text_title)
assert tokens_title[0].text == tokens_lower[0].text.title()
assert tokens_lower[0].text == tokens_title[0].text.lower()
assert tokens_lower[1].text == tokens_title[1].text
@pytest.mark.parametrize("pron", ["I", "You", "He", "She", "It", "We", "They"])
@pytest.mark.parametrize("contraction", ["'ll", "'d"])
def test_en_tokenizer_keeps_title_case(en_tokenizer, pron, contraction):
tokens = en_tokenizer(pron + contraction)
assert tokens[0].text == pron
assert tokens[1].text == contraction
@pytest.mark.parametrize("exc", ["Ill", "ill", "Hell", "hell", "Well", "well"])
def test_en_tokenizer_excludes_ambiguous(en_tokenizer, exc):
tokens = en_tokenizer(exc)
assert len(tokens) == 1
@pytest.mark.parametrize(
"wo_punct,w_punct", [("We've", "`We've"), ("couldn't", "couldn't)")]
)
def test_en_tokenizer_splits_defined_punct(en_tokenizer, wo_punct, w_punct):
tokens = en_tokenizer(wo_punct)
assert len(tokens) == 2
tokens = en_tokenizer(w_punct)
assert len(tokens) == 3
@pytest.mark.parametrize("text", ["e.g.", "p.m.", "Jan.", "Dec.", "Inc."])
def test_en_tokenizer_handles_abbr(en_tokenizer, text):
tokens = en_tokenizer(text)
assert len(tokens) == 1
def test_en_tokenizer_handles_exc_in_text(en_tokenizer):
text = "It's mediocre i.e. bad."
tokens = en_tokenizer(text)
assert len(tokens) == 6
assert tokens[3].text == "i.e."
@pytest.mark.parametrize("text", ["1am", "12a.m.", "11p.m.", "4pm"])
def test_en_tokenizer_handles_times(en_tokenizer, text):
tokens = en_tokenizer(text)
assert len(tokens) == 2
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@pytest.mark.parametrize(
"text,norms", [("I'm", ["i", "am"]), ("shan't", ["shall", "not"])]
)
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def test_en_tokenizer_norm_exceptions(en_tokenizer, text, norms):
tokens = en_tokenizer(text)
assert [token.norm_ for token in tokens] == norms
Reduce stored lexemes data, move feats to lookups (#5238) * Reduce stored lexemes data, move feats to lookups * Move non-derivable lexemes features (`norm / cluster / prob`) to `spacy-lookups-data` as lookups * Get/set `norm` in both lookups and `LexemeC`, serialize in lookups * Remove `cluster` and `prob` from `LexemesC`, get/set/serialize in lookups only * Remove serialization of lexemes data as `vocab/lexemes.bin` * Remove `SerializedLexemeC` * Remove `Lexeme.to_bytes/from_bytes` * Modify normalization exception loading: * Always create `Vocab.lookups` table `lexeme_norm` for normalization exceptions * Load base exceptions from `lang.norm_exceptions`, but load language-specific exceptions from lookups * Set `lex_attr_getter[NORM]` including new lookups table in `BaseDefaults.create_vocab()` and when deserializing `Vocab` * Remove all cached lexemes when deserializing vocab to override existing normalizations with the new normalizations (as a replacement for the previous step that replaced all lexemes data with the deserialized data) * Skip English normalization test Skip English normalization test because the data is now in `spacy-lookups-data`. * Remove norm exceptions Moved to spacy-lookups-data. * Move norm exceptions test to spacy-lookups-data * Load extra lookups from spacy-lookups-data lazily Load extra lookups (currently for cluster and prob) lazily from the entry point `lg_extra` as `Vocab.lookups_extra`. * Skip creating lexeme cache on load To improve model loading times, do not create the full lexeme cache when loading. The lexemes will be created on demand when processing. * Identify numeric values in Lexeme.set_attrs() With the removal of a special case for `PROB`, also identify `float` to avoid trying to convert it with the `StringStore`. * Skip lexeme cache init in from_bytes * Unskip and update lookups tests for python3.6+ * Update vocab pickle to include lookups_extra * Update vocab serialization tests Check strings rather than lexemes since lexemes aren't initialized automatically, account for addition of "_SP". * Re-skip lookups test because of python3.5 * Skip PROB/float values in Lexeme.set_attrs * Convert is_oov from lexeme flag to lex in vectors Instead of storing `is_oov` as a lexeme flag, `is_oov` reports whether the lexeme has a vector. Co-authored-by: Matthew Honnibal <honnibal+gh@gmail.com>
2020-05-19 16:59:14 +03:00
@pytest.mark.skip
@pytest.mark.parametrize(
"text,norm", [("radicalised", "radicalized"), ("cuz", "because")]
)
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def test_en_lex_attrs_norm_exceptions(en_tokenizer, text, norm):
tokens = en_tokenizer(text)
assert tokens[0].norm_ == norm