def get_teacher_forcing_metric(self,
gen_log_prob_key="gen_log_prob",
invalid_vocab=False):
'''Get metrics for teacher-forcing.
It contains:
* :class:`.metric.PerplexityMetric`
Arguments:
gen_log_prob_key (str): The key of predicted log probability over words.
Refer to :class:`.metric.PerplexityMetric`. Default: ``gen_log_prob``.
invalid_vocab (bool): Whether ``gen_log_prob`` contains invalid vocab.
Refer to :class:`.metric.PerplexityMetric`. Default: ``False``.
Returns:
A :class:`.metric.MetricChain` object.
'''
metric = MetricChain()
metric.add_metric(
PerplexityMetric(self,
reference_allvocabs_key="resp_allvocabs",
reference_len_key="resp_length",
gen_log_prob_key=gen_log_prob_key,
invalid_vocab=invalid_vocab))
return metric
def test_close1(self, data_loader):
dataloader = FakeMultiDataloader()
data = dataloader.get_data(reference_key='reference_key', reference_len_key='reference_len_key', \
turn_len_key='turn_len_key', gen_prob_key='gen_prob_key', \
gen_key='gen_key', context_key='context_key')
if data_loader == 'field':
dataloader = dataloader.get_default_field()
pm = MultiTurnPerplexityMetric(dataloader, 'reference_key', 'reference_len_key', 'gen_prob_key', \
generate_rare_vocab=True, full_check=True)
perplexity = TestMultiTurnPerplexityMetric().get_perplexity( \
data, dataloader, True, 'reference_key', 'reference_len_key', 'gen_prob_key')
bcm = MultiTurnBleuCorpusMetric(dataloader, multi_turn_reference_allvocabs_key='reference_key', \
multi_turn_gen_key='gen_key', turn_len_key='turn_len_key')
bleu = TestMultiTurnBleuCorpusMetric().get_bleu(
dataloader, data, 'reference_key', 'gen_key')
_data = copy.deepcopy(data)
mc = MetricChain()
mc.add_metric(pm)
mc.add_metric(bcm)
mc.forward(data)
res = mc.close()
assert np.isclose(res['perplexity'], perplexity)
assert np.isclose(res['bleu'], bleu)
assert same_dict(data, _data)
def get_inference_metric(self, multi_turn_gen_key="multi_turn_gen"):
metric = MetricChain()
metric.add_metric(
MyBleuMetric(self,
multi_turn_gen_key=multi_turn_gen_key,
multi_turn_reference_allvocabs_key="sent_allvocabs",
turn_len_key="turn_length"))
metric.add_metric(
MultiTurnDialogRecorder(
self,
multi_turn_gen_key=multi_turn_gen_key,
multi_turn_reference_allvocabs_key="sent_allvocabs",
turn_len_key="turn_length"))
metric.add_metric(
MyDistinctMetric(self,
multi_turn_gen_key=multi_turn_gen_key,
turn_len_key="turn_length"))
return metric
def get_inference_metric(self, gen_key="gen"):
'''Get metrics for inference.
It contains:
* :class:`.metric.BleuCorpusMetric`
* :class:`.metric.SingleTurnDialogRecorder`
Arguments:
gen_key (str): The key of generated sentences in index form.
Refer to :class:`.metric.BleuCorpusMetric` or
:class:`.metric.SingleTurnDialogRecorder`. Default: ``gen``.
Returns:
A :class:`.metric.MetricChain` object.
'''
metric = MetricChain()
metric.add_metric(BleuCorpusMetric(self, gen_key=gen_key, \
reference_allvocabs_key="resp_allvocabs"))
metric.add_metric(SingleTurnDialogRecorder(self, gen_key=gen_key))
metric.add_metric(SingleTurnDistinct(self, gen_key=gen_key))
return metric
def test_add_metric(self): mc = MetricChain() with pytest.raises(TypeError): mc.add_metric([1, 2, 3])
def test_init(self): mc = MetricChain()
