spaCy/spacy/tests/tokenizer/test_explain.py

146 lines
5.3 KiB
Python

import re
import string
import hypothesis
import hypothesis.strategies
import pytest
import spacy
from spacy.tokenizer import Tokenizer
from spacy.util import get_lang_class
# Only include languages with no external dependencies
# "is" seems to confuse importlib, so we're also excluding it for now
# excluded: ja, ru, th, uk, vi, zh, is
LANGUAGES = [
pytest.param("fr", marks=pytest.mark.slow()),
pytest.param("af", marks=pytest.mark.slow()),
pytest.param("ar", marks=pytest.mark.slow()),
pytest.param("bg", marks=pytest.mark.slow()),
"bn",
pytest.param("bo", marks=pytest.mark.slow()),
pytest.param("ca", marks=pytest.mark.slow()),
pytest.param("cs", marks=pytest.mark.slow()),
pytest.param("da", marks=pytest.mark.slow()),
pytest.param("de", marks=pytest.mark.slow()),
"el",
"en",
pytest.param("es", marks=pytest.mark.slow()),
pytest.param("et", marks=pytest.mark.slow()),
pytest.param("fa", marks=pytest.mark.slow()),
pytest.param("fi", marks=pytest.mark.slow()),
"fr",
pytest.param("ga", marks=pytest.mark.slow()),
pytest.param("he", marks=pytest.mark.slow()),
pytest.param("hi", marks=pytest.mark.slow()),
pytest.param("hr", marks=pytest.mark.slow()),
"hu",
pytest.param("id", marks=pytest.mark.slow()),
pytest.param("it", marks=pytest.mark.slow()),
pytest.param("kn", marks=pytest.mark.slow()),
pytest.param("lb", marks=pytest.mark.slow()),
pytest.param("lt", marks=pytest.mark.slow()),
pytest.param("lv", marks=pytest.mark.slow()),
pytest.param("nb", marks=pytest.mark.slow()),
pytest.param("nl", marks=pytest.mark.slow()),
"pl",
pytest.param("pt", marks=pytest.mark.slow()),
pytest.param("ro", marks=pytest.mark.slow()),
pytest.param("si", marks=pytest.mark.slow()),
pytest.param("sk", marks=pytest.mark.slow()),
pytest.param("sl", marks=pytest.mark.slow()),
pytest.param("sq", marks=pytest.mark.slow()),
pytest.param("sr", marks=pytest.mark.slow()),
pytest.param("sv", marks=pytest.mark.slow()),
pytest.param("ta", marks=pytest.mark.slow()),
pytest.param("te", marks=pytest.mark.slow()),
pytest.param("tl", marks=pytest.mark.slow()),
pytest.param("tr", marks=pytest.mark.slow()),
pytest.param("tt", marks=pytest.mark.slow()),
pytest.param("ur", marks=pytest.mark.slow()),
pytest.param("kmr", marks=pytest.mark.slow()),
]
@pytest.mark.parametrize("lang", LANGUAGES)
def test_tokenizer_explain(lang):
tokenizer = get_lang_class(lang)().tokenizer
examples = pytest.importorskip(f"spacy.lang.{lang}.examples")
for sentence in examples.sentences:
tokens = [t.text for t in tokenizer(sentence) if not t.is_space]
debug_tokens = [t[1] for t in tokenizer.explain(sentence)]
assert tokens == debug_tokens
def test_tokenizer_explain_special_matcher(en_vocab):
suffix_re = re.compile(r"[\.]$")
infix_re = re.compile(r"[/]")
rules = {"a.": [{"ORTH": "a."}]}
tokenizer = Tokenizer(
en_vocab,
rules=rules,
suffix_search=suffix_re.search,
infix_finditer=infix_re.finditer,
)
tokens = [t.text for t in tokenizer("a/a.")]
explain_tokens = [t[1] for t in tokenizer.explain("a/a.")]
assert tokens == explain_tokens
def test_tokenizer_explain_special_matcher_whitespace(en_vocab):
rules = {":]": [{"ORTH": ":]"}]}
tokenizer = Tokenizer(
en_vocab,
rules=rules,
)
text = ": ]"
tokens = [t.text for t in tokenizer(text)]
explain_tokens = [t[1] for t in tokenizer.explain(text)]
assert tokens == explain_tokens
@hypothesis.strategies.composite
def sentence_strategy(draw: hypothesis.strategies.DrawFn, max_n_words: int = 4) -> str:
"""
Composite strategy for fuzzily generating sentence with varying interpunctation.
draw (hypothesis.strategies.DrawFn): Protocol for drawing function allowing to fuzzily pick from hypothesis'
strategies.
max_n_words (int): Max. number of words in generated sentence.
RETURNS (str): Fuzzily generated sentence.
"""
punctuation_and_space_regex = "|".join(
[*[re.escape(p) for p in string.punctuation], r"\s"]
)
sentence = [
[
draw(hypothesis.strategies.text(min_size=1)),
draw(hypothesis.strategies.from_regex(punctuation_and_space_regex)),
]
for _ in range(
draw(hypothesis.strategies.integers(min_value=2, max_value=max_n_words))
)
]
return " ".join([token for token_pair in sentence for token in token_pair])
@pytest.mark.xfail
@pytest.mark.parametrize("lang", LANGUAGES)
@hypothesis.given(sentence=sentence_strategy())
def test_tokenizer_explain_fuzzy(lang: str, sentence: str) -> None:
"""
Tests whether output of tokenizer.explain() matches tokenizer output. Input generated by hypothesis.
lang (str): Language to test.
text (str): Fuzzily generated sentence to tokenize.
"""
tokenizer: Tokenizer = spacy.blank(lang).tokenizer
# Tokenizer.explain is not intended to handle whitespace or control
# characters in the same way as Tokenizer
sentence = re.sub(r"\s+", " ", sentence).strip()
tokens = [t.text for t in tokenizer(sentence)]
debug_tokens = [t[1] for t in tokenizer.explain(sentence)]
assert tokens == debug_tokens, f"{tokens}, {debug_tokens}, {sentence}"