spaCy/spacy/tests/test_lemmatizer.py
adrianeboyd a5cd203284
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 15:59:14 +02:00

50 lines
1.9 KiB
Python

# coding: utf8
from __future__ import unicode_literals
import pytest
from spacy.tokens import Doc
from spacy.language import Language
from spacy.lookups import Lookups
def test_lemmatizer_reflects_lookups_changes():
"""Test for an issue that'd cause lookups available in a model loaded from
disk to not be reflected in the lemmatizer."""
nlp = Language()
assert Doc(nlp.vocab, words=["foo"])[0].lemma_ == "foo"
table = nlp.vocab.lookups.add_table("lemma_lookup")
table["foo"] = "bar"
assert Doc(nlp.vocab, words=["foo"])[0].lemma_ == "bar"
table = nlp.vocab.lookups.get_table("lemma_lookup")
table["hello"] = "world"
# The update to the table should be reflected in the lemmatizer
assert Doc(nlp.vocab, words=["hello"])[0].lemma_ == "world"
new_nlp = Language()
table = new_nlp.vocab.lookups.add_table("lemma_lookup")
table["hello"] = "hi"
assert Doc(new_nlp.vocab, words=["hello"])[0].lemma_ == "hi"
nlp_bytes = nlp.to_bytes()
new_nlp.from_bytes(nlp_bytes)
# Make sure we have the previously saved lookup table
assert "lemma_lookup" in new_nlp.vocab.lookups
assert len(new_nlp.vocab.lookups.get_table("lemma_lookup")) == 2
assert new_nlp.vocab.lookups.get_table("lemma_lookup")["hello"] == "world"
assert Doc(new_nlp.vocab, words=["foo"])[0].lemma_ == "bar"
assert Doc(new_nlp.vocab, words=["hello"])[0].lemma_ == "world"
def test_tagger_warns_no_lemma_lookups():
nlp = Language()
nlp.vocab.lookups = Lookups()
assert not len(nlp.vocab.lookups)
tagger = nlp.create_pipe("tagger")
with pytest.warns(UserWarning):
tagger.begin_training()
nlp.add_pipe(tagger)
with pytest.warns(UserWarning):
nlp.begin_training()
nlp.vocab.lookups.add_table("lemma_lookup")
with pytest.warns(None) as record:
nlp.begin_training()
assert not record.list