def test_token_cooccurrence_vectorizer_orientation():
vectorizer = TokenCooccurrenceVectorizer(window_radius=1,
window_orientation="directional")
result = vectorizer.fit_transform(text_token_data)
assert result.shape == (4, 8)
# Check the pok preceded by wer value is 1
row = vectorizer.token_label_dictionary_["pok"]
col = vectorizer.column_label_dictionary_["pre_wer"]
assert result[row, col] == 1
result_before = TokenCooccurrenceVectorizer(
window_orientation="before").fit_transform(text_token_data)
result_after = TokenCooccurrenceVectorizer(
window_orientation="after").fit_transform(text_token_data)
assert np.all(
result_after.toarray() == (result_before.transpose()).toarray())
result_symmetric = TokenCooccurrenceVectorizer(
window_orientation="symmetric").fit_transform(text_token_data)
assert np.all(result_symmetric.toarray() == (result_before +
result_after).toarray())
def test_token_cooccurrence_vectorizer_orientation():
vectorizer = TokenCooccurrenceVectorizer(
window_radii=1, window_orientations="directional", normalize_windows=False
)
result = vectorizer.fit_transform(text_token_data)
assert result.shape == (4, 8)
# Check the pok preceded by wer value is 1
row = vectorizer.token_label_dictionary_["pok"]
col = vectorizer.column_label_dictionary_["pre_0_wer"]
assert result[row, col] == 1
result_before = TokenCooccurrenceVectorizer(
window_radii=1, window_orientations="before", normalize_windows=False
).fit_transform(text_token_data)
result_after = TokenCooccurrenceVectorizer(
window_radii=1, window_orientations="after", normalize_windows=False
).fit_transform(text_token_data)
assert np.all(result_after.toarray() == (result_before.transpose()).toarray())
assert np.all(
result.toarray() == np.hstack([result_before.toarray(), result_after.toarray()])
)