def _test(metric_device):
engine = Engine(update)
m = Bleu(ngram=4, smooth="smooth2", average="micro")
m.attach(engine, "bleu")
engine.run(data=list(range(size)), max_epochs=1)
assert "bleu" in engine.state.metrics
ref_bleu = 0
references = []
candidates = []
for _candidates, _references in data:
references.append(_references[0])
candidates.append(_candidates[0])
with warnings.catch_warnings():
warnings.simplefilter("ignore")
ref_bleu += corpus_bleu(
references,
candidates,
weights=[0.25, 0.25, 0.25, 0.25],
smoothing_function=SmoothingFunction().method2,
)
assert pytest.approx(engine.state.metrics["bleu"]) == ref_bleu
def _test(metric_device):
engine = Engine(update)
m = Bleu(ngram=4, smooth="smooth2")
m.attach(engine, "bleu")
engine.run(data=list(range(size)), max_epochs=1)
assert "bleu" in engine.state.metrics
ref_bleu = 0
for candidates, references in data:
with warnings.catch_warnings():
warnings.simplefilter("ignore")
ref_bleu += sentence_bleu(
references[0],
candidates[0],
weights=[0.25, 0.25, 0.25, 0.25],
smoothing_function=SmoothingFunction().method2,
)
assert pytest.approx(
engine.state.metrics["bleu"]) == ref_bleu / len(data)