mirror of
https://github.com/explosion/spaCy.git
synced 2024-11-13 13:17:06 +03:00
a5cd203284
* 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>
128 lines
4.0 KiB
Python
128 lines
4.0 KiB
Python
# coding: utf-8
|
|
from __future__ import unicode_literals
|
|
|
|
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'"])
|
|
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"])
|
|
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"
|
|
assert tokens[1].lemma_ == "will"
|
|
|
|
|
|
@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
|
|
assert tokens[1].lemma_ in ["a.m.", "p.m."]
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"text,norms", [("I'm", ["i", "am"]), ("shan't", ["shall", "not"])]
|
|
)
|
|
def test_en_tokenizer_norm_exceptions(en_tokenizer, text, norms):
|
|
tokens = en_tokenizer(text)
|
|
assert [token.norm_ for token in tokens] == norms
|
|
|
|
|
|
@pytest.mark.skip
|
|
@pytest.mark.parametrize(
|
|
"text,norm", [("radicalised", "radicalized"), ("cuz", "because")]
|
|
)
|
|
def test_en_lex_attrs_norm_exceptions(en_tokenizer, text, norm):
|
|
tokens = en_tokenizer(text)
|
|
assert tokens[0].norm_ == norm
|