def test_bleu():
bleu = Bleu(ngram=4, smooth="smooth2")
bleu.update((corpus.cand_1, corpus.references_1))
bleu.update((corpus.cand_2a, corpus.references_2))
bleu.update((corpus.cand_2b, corpus.references_2))
bleu.update((corpus.cand_3, corpus.references_2))
value = bleu._corpus_bleu([corpus.references_1], [corpus.cand_1])
value += bleu._corpus_bleu([corpus.references_2], [corpus.cand_2a])
value += bleu._corpus_bleu([corpus.references_2], [corpus.cand_2b])
value += bleu._corpus_bleu([corpus.references_2], [corpus.cand_3])
assert bleu.compute() == value / 4
def _test(candidates,
references,
average,
smooth="no_smooth",
smooth_nltk_fn=None,
ngram_range=8):
for i in range(1, ngram_range):
weights = tuple([1 / i] * i)
bleu = Bleu(ngram=i, average=average, smooth=smooth)
if average == "macro":
with warnings.catch_warnings():
warnings.simplefilter("ignore")
reference = sentence_bleu(references[0],
candidates[0],
weights=weights,
smoothing_function=smooth_nltk_fn)
assert pytest.approx(reference) == bleu._sentence_bleu(
references[0], candidates[0])
elif average == "micro":
with warnings.catch_warnings():
warnings.simplefilter("ignore")
reference = corpus_bleu(references,
candidates,
weights=weights,
smoothing_function=smooth_nltk_fn)
assert pytest.approx(reference) == bleu._corpus_bleu(
references, candidates)
bleu.update((candidates, references))
assert pytest.approx(reference) == bleu.compute()
def test_corpus_bleu(candidate, references):
print(candidate, references)
for i in range(1, 8):
weights = tuple([1 / i] * i)
with warnings.catch_warnings():
warnings.simplefilter("ignore")
reference = corpus_bleu(references, candidate, weights=weights)
bleu = Bleu(ngram=i)
assert pytest.approx(reference) == bleu._corpus_bleu(
references, candidate)
bleu.update((candidate[0], references[0]))
assert pytest.approx(reference) == bleu.compute()
def test_accumulation_micro_bleu():
bleu = Bleu(ngram=4, smooth="smooth2", average="micro")
bleu.update(([corpus.cand_1], [corpus.references_1]))
bleu.update(([corpus.cand_2a], [corpus.references_2]))
bleu.update(([corpus.cand_2b], [corpus.references_2]))
bleu.update(([corpus.cand_3], [corpus.references_2]))
value = bleu._corpus_bleu(
[
corpus.references_1, corpus.references_2, corpus.references_2,
corpus.references_2
],
[corpus.cand_1, corpus.cand_2a, corpus.cand_2b, corpus.cand_3],
)
assert bleu.compute() == value
def test_corpus_bleu_nltk_smooth2(candidate, references):
for i in range(1, 8):
weights = tuple([1 / i] * i)
with warnings.catch_warnings():
warnings.simplefilter("ignore")
reference = corpus_bleu(
references,
candidate,
weights=weights,
smoothing_function=SmoothingFunction().method2)
bleu = Bleu(ngram=i, smooth="nltk_smooth2")
assert reference == bleu._corpus_bleu(references, candidate)
bleu.update((candidate[0], references[0]))
assert reference == bleu.compute()
def test_bleu_batch_micro():
bleu = Bleu(ngram=4, average="micro")
# Batch size 3
hypotheses = [corpus.cand_1, corpus.cand_2a, corpus.cand_2b]
refs = [corpus.references_1, corpus.references_2, corpus.references_2]
bleu.update((hypotheses, refs))
with warnings.catch_warnings():
warnings.simplefilter("ignore")
reference_bleu_score = corpus_bleu(refs, hypotheses)
assert pytest.approx(bleu.compute()) == reference_bleu_score
assert pytest.approx(bleu._corpus_bleu(refs,
hypotheses)) == reference_bleu_score