spaCy/spacy/tests/lang/zh/test_tokenizer.py
Ines Montani 43b960c01b
Refactor pipeline components, config and language data (#5759)
* Update with WIP

* Update with WIP

* Update with pipeline serialization

* Update types and pipe factories

* Add deep merge, tidy up and add tests

* Fix pipe creation from config

* Don't validate default configs on load

* Update spacy/language.py

Co-authored-by: Ines Montani <ines@ines.io>

* Adjust factory/component meta error

* Clean up factory args and remove defaults

* Add test for failing empty dict defaults

* Update pipeline handling and methods

* provide KB as registry function instead of as object

* small change in test to make functionality more clear

* update example script for EL configuration

* Fix typo

* Simplify test

* Simplify test

* splitting pipes.pyx into separate files

* moving default configs to each component file

* fix batch_size type

* removing default values from component constructors where possible (TODO: test 4725)

* skip instead of xfail

* Add test for config -> nlp with multiple instances

* pipeline.pipes -> pipeline.pipe

* Tidy up, document, remove kwargs

* small cleanup/generalization for Tok2VecListener

* use DEFAULT_UPSTREAM field

* revert to avoid circular imports

* Fix tests

* Replace deprecated arg

* Make model dirs require config

* fix pickling of keyword-only arguments in constructor

* WIP: clean up and integrate full config

* Add helper to handle function args more reliably

Now also includes keyword-only args

* Fix config composition and serialization

* Improve config debugging and add visual diff

* Remove unused defaults and fix type

* Remove pipeline and factories from meta

* Update spacy/default_config.cfg

Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>

* Update spacy/default_config.cfg

* small UX edits

* avoid printing stack trace for debug CLI commands

* Add support for language-specific factories

* specify the section of the config which holds the model to debug

* WIP: add Language.from_config

* Update with language data refactor WIP

* Auto-format

* Add backwards-compat handling for Language.factories

* Update morphologizer.pyx

* Fix morphologizer

* Update and simplify lemmatizers

* Fix Japanese tests

* Port over tagger changes

* Fix Chinese and tests

* Update to latest Thinc

* WIP: xfail first Russian lemmatizer test

* Fix component-specific overrides

* fix nO for output layers in debug_model

* Fix default value

* Fix tests and don't pass objects in config

* Fix deep merging

* Fix lemma lookup data registry

Only load the lookups if an entry is available in the registry (and if spacy-lookups-data is installed)

* Add types

* Add Vocab.from_config

* Fix typo

* Fix tests

* Make config copying more elegant

* Fix pipe analysis

* Fix lemmatizers and is_base_form

* WIP: move language defaults to config

* Fix morphology type

* Fix vocab

* Remove comment

* Update to latest Thinc

* Add morph rules to config

* Tidy up

* Remove set_morphology option from tagger factory

* Hack use_gpu

* Move [pipeline] to top-level block and make [nlp.pipeline] list

Allows separating component blocks from component order – otherwise, ordering the config would mean a changed component order, which is bad. Also allows initial config to define more components and not use all of them

* Fix use_gpu and resume in CLI

* Auto-format

* Remove resume from config

* Fix formatting and error

* [pipeline] -> [components]

* Fix types

* Fix tagger test: requires set_morphology?

Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>
Co-authored-by: svlandeg <sofie.vanlandeghem@gmail.com>
Co-authored-by: Matthew Honnibal <honnibal+gh@gmail.com>
2020-07-22 13:42:59 +02:00

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import pytest
from spacy.lang.zh import Chinese, _get_pkuseg_trie_data
from thinc.config import ConfigValidationError
# fmt: off
TEXTS = ("作为语言而言,为世界使用人数最多的语言,目前世界有五分之一人口做为母语。",)
JIEBA_TOKENIZER_TESTS = [
(TEXTS[0],
['作为', '语言', '而言', '', '', '世界', '使用', '', '数最多',
'', '语言', '', '目前', '世界', '', '五分之一', '人口', '',
'', '母语', '']),
]
PKUSEG_TOKENIZER_TESTS = [
(TEXTS[0],
['作为', '语言', '而言', '', '', '世界', '使用', '人数', '最多',
'', '语言', '', '目前', '世界', '', '五分之一', '人口', '做为',
'母语', '']),
]
# fmt: on
@pytest.mark.parametrize("text", TEXTS)
def test_zh_tokenizer_char(zh_tokenizer_char, text):
tokens = [token.text for token in zh_tokenizer_char(text)]
assert tokens == list(text)
@pytest.mark.parametrize("text,expected_tokens", JIEBA_TOKENIZER_TESTS)
def test_zh_tokenizer_jieba(zh_tokenizer_jieba, text, expected_tokens):
tokens = [token.text for token in zh_tokenizer_jieba(text)]
assert tokens == expected_tokens
@pytest.mark.parametrize("text,expected_tokens", PKUSEG_TOKENIZER_TESTS)
def test_zh_tokenizer_pkuseg(zh_tokenizer_pkuseg, text, expected_tokens):
tokens = [token.text for token in zh_tokenizer_pkuseg(text)]
assert tokens == expected_tokens
def test_zh_tokenizer_pkuseg_user_dict(zh_tokenizer_pkuseg, zh_tokenizer_char):
user_dict = _get_pkuseg_trie_data(zh_tokenizer_pkuseg.pkuseg_seg.preprocesser.trie)
zh_tokenizer_pkuseg.pkuseg_update_user_dict(["nonsense_asdf"])
updated_user_dict = _get_pkuseg_trie_data(
zh_tokenizer_pkuseg.pkuseg_seg.preprocesser.trie
)
assert len(user_dict) == len(updated_user_dict) - 1
# reset user dict
zh_tokenizer_pkuseg.pkuseg_update_user_dict([], reset=True)
reset_user_dict = _get_pkuseg_trie_data(
zh_tokenizer_pkuseg.pkuseg_seg.preprocesser.trie
)
assert len(reset_user_dict) == 0
# warn if not relevant
with pytest.warns(UserWarning):
zh_tokenizer_char.pkuseg_update_user_dict(["nonsense_asdf"])
def test_zh_extra_spaces(zh_tokenizer_char):
# note: three spaces after "I"
tokens = zh_tokenizer_char("I like cheese.")
assert tokens[1].orth_ == " "
def test_zh_unsupported_segmenter():
config = {"nlp": {"tokenizer": {"segmenter": "unk"}}}
with pytest.raises(ConfigValidationError):
Chinese.from_config(config)
def test_zh_uninitialized_pkuseg():
config = {"nlp": {"tokenizer": {"segmenter": "char"}}}
nlp = Chinese.from_config(config)
nlp.tokenizer.segmenter = "pkuseg"
with pytest.raises(ValueError):
nlp("test")