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

133 lines
4.2 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.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
@pytest.mark.parametrize("text", ["faster", "fastest", "better", "best"])
def test_en_lemmatizer_handles_irreg_adverbs(en_tokenizer, text):
tokens = en_tokenizer(text)
assert tokens[0].lemma_ in ["fast", "well"]