def spacy_doc():
ds = datasets.CapitolWords()
text = next(ds.texts(min_len=1500, limit=1))
return textacy.make_spacy_doc(text, lang="en")
import pytest
import textacy
from textacy import datasets
from textacy.extract import keyterms as kt
DATASET = datasets.CapitolWords()
pytestmark = pytest.mark.skipif(
DATASET.filepath is None,
reason="CapitolWords dataset must be downloaded before running tests",
)
@pytest.fixture(scope="module")
def spacy_doc():
text = next(DATASET.texts(min_len=1500, limit=1))
return textacy.make_spacy_doc(text, lang="en_core_web_sm")
@pytest.fixture(scope="module")
def empty_spacy_doc():
return textacy.make_spacy_doc("", lang="en_core_web_sm")
def test_default(spacy_doc):
result = kt.sgrank(spacy_doc)
assert isinstance(result, list) and len(result) > 0
assert all(isinstance(ts, tuple) and len(ts) == 2 for ts in result)
assert all(
isinstance(ts[0], str) and isinstance(ts[1], float) for ts in result)