def __init__(self, **kwargs: Dict[str, Any]):
self.training_metrics = {}
self.validation_metrics = {}
for name, metric_kwargs in kwargs.items():
# We need a special logic for the vocabulary, we do not want to deep copy it,
# and it cannot be used in Params
vocab = metric_kwargs.pop("vocabulary", None)
self.training_metrics[name] = Metric.from_params(
Params(copy.deepcopy(metric_kwargs)),
**{} if vocab is None else {"vocabulary": vocab})
self.validation_metrics[name] = Metric.from_params(
Params(metric_kwargs),
**{} if vocab is None else {"vocabulary": vocab})
def test_span_f1_can_build_from_params(self):
params = Params(
{"type": "non_bio_span_f1", "tag_namespace": "tags", "ignore_classes": ["V"]})
metric = Metric.from_params(params, self.vocab)
assert metric._ignore_classes == ["V"]
assert metric._label_vocabulary == self.vocab.get_index_to_token_vocabulary(
"tags")
def test_span_f1_can_build_from_params(self, device: str):
params = Params({
"type": "span_f1",
"tag_namespace": "tags",
"ignore_classes": ["V"]
})
metric = Metric.from_params(params=params, vocabulary=self.vocab)
assert metric._ignore_classes == ["V"] # type: ignore
assert metric._label_vocabulary == self.vocab.get_index_to_token_vocabulary( # type: ignore
"tags")
def from_params(cls, vocab, params):
text_field_embedder = TextFieldEmbedder.from_params(
vocab, params.pop('text_field_embedder'))
hidden_size = params.pop('hidden_size', 128)
num_layers = params.pop('num_layers', 2)
dropout = params.pop('dropout', 0.5)
tag_namespace = params.pop('tag_namespace', 'tags')
initializer = None
initializer_params = params.pop('initializer', None)
if initializer_params is not None:
initializer = Initializer.from_params(initializer_params)
metric = None
metric_params = params.pop('metric', None)
if metric_params is not None:
metric = Metric.from_params(metric_params)
params.assert_empty(cls.__name__)
return cls(vocab, text_field_embedder, hidden_size=hidden_size, num_layers=num_layers,
dropout=dropout, tag_namespace=tag_namespace, initializer=initializer,
metric=metric)
def test_span_f1_can_build_from_params(self):
params = Params({"type": "span_f1", "tag_namespace": "tags", "ignore_classes": ["V"]})
metric = Metric.from_params(params=params, vocabulary=self.vocab)
assert metric._ignore_classes == ["V"]
assert metric._label_vocabulary == self.vocab.get_index_to_token_vocabulary("tags")
def test_span_f1_can_build_from_params(self):
params = Params({u"type": u"span_f1", u"tag_namespace": u"tags", u"ignore_classes": [u"V"]})
metric = Metric.from_params(params=params, vocabulary=self.vocab)
assert metric._ignore_classes == [u"V"]
assert metric._label_vocabulary == self.vocab.get_index_to_token_vocabulary(u"tags")