Adding New Metrics

VizSeq has an open API for adding user-defined metrics. You are welcomed to contribute new scorers to enlarge VizSeq's metric coverage!

Implementing A New Scorer Class

To start with, first add to vizseq/scorers, in which a new scorer class is inherited from VizSeqScorer and a score method is defined. And then register the new scorer class with an id and a name using vizseq.scorers.register_scorer:

from typing import Optional, List
from vizseq.scorers import register_scorer, VizSeqScorer, VizSeqScore
@register_scorer('new_metric_id', 'New Metric Name')
class NewMetricScorer(VizSeqScorer):
def score(
self, hypothesis: List[str], references: List[List[str]],
tags: Optional[List[List[str]]] = None
) -> VizSeqScore:
# calculate the number of workers by number of examples
corpus_score, group_scores, sent_scores = None, None, None
if self.corpus_level:
# implement corpus-level score
corpus_score = 99.9
if self.sent_level:
# implement sentence-level score
sent_scores=[99.9, 99.9]
if tags is not None:
tag_set = self._unique(tags)
# implement group-level (by sentence tags) score
group_scores={t: 99.9 for t in tag_set}
return VizSeqScore.make(
corpus_score=corpus_score, sent_scores=sent_scores,

Testing the New Scorer Class

All the scorer classes need to be covered by tests. To achieve that, Add a unit test to tests/scorers and run:

$ python -m unittest tests.scorers.test_new_metric